all Matlab related stuff have been moved to wrappers

45 files changed, 2 insertions, 1897 deletions

diff --git a/Core/regularizers_CPU/PatchBased_Regul.c b/Core/regularizers_CPU/PatchBased_Regul.c
deleted file mode 100644
index 5d89b0c..0000000
--- a/Core/regularizers_CPU/PatchBased_Regul.c
+++ /dev/null
@@ -1,140 +0,0 @@
-/*
-This work is part of the Core Imaging Library developed by
-Visual Analytics and Imaging System Group of the Science Technology
-Facilities Council, STFC
-
-Copyright 2017 Daniil Kazantsev
-Copyright 2017 Srikanth Nagella, Edoardo Pasca
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-*/
-
-#include "mex.h"
-#include "matrix.h"
-#include "PatchBased_Regul_core.h"
-
-
-/* C-OMP implementation of  patch-based (PB) regularization (2D and 3D cases). 
- * This method finds self-similar patches in data and performs one fixed point iteration to mimimize the PB penalty function
- * 
- * References: 1. Yang Z. & Jacob M. "Nonlocal Regularization of Inverse Problems"
- *             2. Kazantsev D. et al. "4D-CT reconstruction with unified spatial-temporal patch-based regularization"
- *
- * Input Parameters:
- * 1. Image (2D or 3D) [required]
- * 2. ratio of the searching window (e.g. 3 = (2*3+1) = 7 pixels window) [optional]
- * 3. ratio of the similarity window (e.g. 1 = (2*1+1) = 3 pixels window) [optional]
- * 4. h - parameter for the PB penalty function [optional]
- * 5. lambda - regularization parameter  [optional]
-
- * Output:
- * 1. regularized (denoised) Image (N x N)/volume (N x N x N)
- *
- * 2D denoising example in Matlab:   
-   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-   ImDen = PatchBased_Regul(single(u0), 3, 1, 0.08, 0.05); 
- *
- * Matlab + C/mex compilers needed
- * to compile with OMP support: mex PatchBased_Regul.c CFLAGS="\$CFLAGS -fopenmp -Wall" LDFLAGS="\$LDFLAGS -fopenmp"
- *
- * D. Kazantsev *
- * 02/07/2014
- * Harwell, UK
- */
-
-
-void mexFunction(
-        int nlhs, mxArray *plhs[],
-        int nrhs, const mxArray *prhs[]) 
-{    
-    int N, M, Z, numdims, SearchW, SimilW, SearchW_real, padXY, newsizeX, newsizeY, newsizeZ, switchpad_crop;
-    const int  *dims;
-    float *A, *B=NULL, *Ap=NULL, *Bp=NULL, h, lambda;
-    
-    numdims = mxGetNumberOfDimensions(prhs[0]);
-    dims = mxGetDimensions(prhs[0]);
-    
-    N = dims[0];
-    M = dims[1];
-    Z = dims[2];
-    
-    if ((numdims < 2) || (numdims > 3)) {mexErrMsgTxt("The input is 2D image or 3D volume");}
-    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input in single precision is required"); }
-    
-    if(nrhs != 5) mexErrMsgTxt("Five inputs reqired: Image(2D,3D), SearchW, SimilW, Threshold, Regularization parameter");
-    
-    /*Handling inputs*/
-    A  = (float *) mxGetData(prhs[0]);    /* the image/volume to regularize/filter */
-    SearchW_real = 3; /*default value*/
-    SimilW = 1; /*default value*/
-    h = 0.1; 
-    lambda = 0.1;
-    
-    if ((nrhs == 2) || (nrhs == 3) || (nrhs == 4) || (nrhs == 5))   SearchW_real  = (int) mxGetScalar(prhs[1]); /* the searching window ratio */
-    if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5))   SimilW =  (int) mxGetScalar(prhs[2]);  /* the similarity window ratio */
-    if ((nrhs == 4) || (nrhs == 5))  h =  (float) mxGetScalar(prhs[3]);  /* parameter for the PB filtering function */
-    if ((nrhs == 5))  lambda = (float) mxGetScalar(prhs[4]); /* regularization parameter */   
-
-
-    if (h <= 0) mexErrMsgTxt("Parmeter for the PB penalty function should be > 0");
-    if (lambda <= 0) mexErrMsgTxt(" Regularization parmeter should be > 0");
-       
-    SearchW = SearchW_real + 2*SimilW;
-    
-    /* SearchW_full = 2*SearchW + 1; */ /* the full searching window  size */
-    /* SimilW_full = 2*SimilW + 1;  */  /* the full similarity window  size */
-    
-    padXY = SearchW + 2*SimilW; /* padding sizes */
-    newsizeX = N + 2*(padXY); /* the X size of the padded array */
-    newsizeY = M + 2*(padXY); /* the Y size of the padded array */
-    newsizeZ = Z + 2*(padXY); /* the Z size of the padded array */
-    int N_dims[] = {newsizeX, newsizeY, newsizeZ};
-    
-    /******************************2D case ****************************/
-    if (numdims == 2) {
-        /*Handling output*/
-        B = (float*)mxGetData(plhs[0] = mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
-        /*allocating memory for the padded arrays */
-        Ap = (float*)mxGetData(mxCreateNumericMatrix(newsizeX, newsizeY, mxSINGLE_CLASS, mxREAL));
-        Bp = (float*)mxGetData(mxCreateNumericMatrix(newsizeX, newsizeY, mxSINGLE_CLASS, mxREAL));
-        /**************************************************************************/
-        /*Perform padding of image A to the size of [newsizeX * newsizeY] */
-        switchpad_crop = 0; /*padding*/
-        pad_crop(A, Ap, M, N, 0, newsizeY, newsizeX, 0, padXY, switchpad_crop);
-        
-        /* Do PB regularization with the padded array  */
-        PB_FUNC2D(Ap, Bp, newsizeY, newsizeX, padXY, SearchW, SimilW, (float)h, (float)lambda);
-        
-        switchpad_crop = 1; /*cropping*/
-        pad_crop(Bp, B, M, N, 0, newsizeY, newsizeX, 0, padXY, switchpad_crop);
-    }
-    else
-    {
-        /******************************3D case ****************************/
-        /*Handling output*/
-        B = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dims, mxSINGLE_CLASS, mxREAL));
-        /*allocating memory for the padded arrays */
-        Ap = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
-        Bp = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
-        /**************************************************************************/
-        
-        /*Perform padding of image A to the size of [newsizeX * newsizeY * newsizeZ] */
-        switchpad_crop = 0; /*padding*/
-        pad_crop(A, Ap, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
-        
-        /* Do PB regularization with the padded array  */
-        PB_FUNC3D(Ap, Bp, newsizeY, newsizeX, newsizeZ, padXY, SearchW, SimilW, (float)h, (float)lambda);
-        
-        switchpad_crop = 1; /*cropping*/
-        pad_crop(Bp, B, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
-    } /*end else ndims*/ 
-}    
diff --git a/Core/regularizers_GPU/Diffus_HO/Diff4thHajiaboli_GPU.cpp b/Core/regularizers_GPU/Diffus_HO/Diff4thHajiaboli_GPU.cpp
deleted file mode 100644
index e5b1ee0..0000000
--- a/Core/regularizers_GPU/Diffus_HO/Diff4thHajiaboli_GPU.cpp
+++ /dev/null
@@ -1,114 +0,0 @@
-#include "mex.h"
-#include <matrix.h>
-#include <math.h>
-#include <stdlib.h>
-#include <memory.h>
-#include <stdio.h>
-#include <iostream>
-#include "Diff4th_GPU_kernel.h"
-
-/*
- * 2D and 3D CUDA implementation of the 4th order PDE denoising model by Hajiaboli
- *
- * Reference :
- * "An anisotropic fourth-order diffusion filter for image noise removal" by M. Hajiaboli
- * 
- * Example
- * figure;
- * Im = double(imread('lena_gray_256.tif'))/255;  % loading image
- * u0 = Im + .05*randn(size(Im)); % adding noise
- * u = Diff4thHajiaboli_GPU(single(u0), 0.02, 150);
- * subplot (1,2,1); imshow(u0,[ ]); title('Noisy Image')
- * subplot (1,2,2); imshow(u,[ ]); title('Denoised Image')
- *
- *
- * Linux/Matlab compilation:
- * compile in terminal: nvcc -Xcompiler -fPIC -shared -o Diff4th_GPU_kernel.o Diff4th_GPU_kernel.cu
- * then compile in Matlab: mex -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart Diff4thHajiaboli_GPU.cpp Diff4th_GPU_kernel.o
- */
-
-void mexFunction(
-        int nlhs, mxArray *plhs[],
-        int nrhs, const mxArray *prhs[])
-{
-    int numdims, dimZ, size;
-    float *A, *B, *A_L, *B_L;
-    const int *dims;    
-    
-    numdims = mxGetNumberOfDimensions(prhs[0]);
-    dims = mxGetDimensions(prhs[0]);       
-    
-    float sigma = (float)mxGetScalar(prhs[1]); /* edge-preserving parameter */
-    float lambda = (float)mxGetScalar(prhs[2]); /* regularization parameter */
-    int iter = (int)mxGetScalar(prhs[3]); /* iterations number */
-    
-    if (numdims == 2)  {
-        
-        int N, M, Z, i, j;
-        Z = 0; // for the 2D case
-        float tau = 0.01; // time step is sufficiently small for an explicit methods
-        
-        /*Input data*/
-        A = (float*)mxGetData(prhs[0]);        
-        N = dims[0] + 2;
-        M = dims[1] + 2;
-        A_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
-        B_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
-        
-        /*Output data*/
-        B = (float*)mxGetData(plhs[0] = mxCreateNumericMatrix(dims[0], dims[1], mxSINGLE_CLASS, mxREAL));        
-        
-        // copy A to the bigger A_L with boundaries
-        #pragma omp parallel for shared(A_L, A) private(i,j)
-        for (i=0; i < N; i++) {
-            for (j=0; j < M; j++) {
-                if (((i > 0) && (i < N-1)) &&  ((j > 0) && (j < M-1)))  A_L[i*M+j] = A[(i-1)*(dims[1])+(j-1)];
-            }}
-        
-        // Running CUDA code here
-        Diff4th_GPU_kernel(A_L, B_L, N, M, Z, (float)sigma, iter, (float)tau, lambda);
-        
-        // copy the processed B_L to a smaller B
-        #pragma omp parallel for shared(B_L, B) private(i,j)
-        for (i=0; i < N; i++) {
-            for (j=0; j < M; j++) {
-                if (((i > 0) && (i < N-1)) &&  ((j > 0) && (j < M-1)))   B[(i-1)*(dims[1])+(j-1)] = B_L[i*M+j];
-            }}
-    }
-    if (numdims == 3)  {
-        //  3D image denoising / regularization
-        int N, M, Z, i, j, k;
-        float tau = 0.0007; // Time Step is small for an explicit methods
-        A = (float*)mxGetData(prhs[0]);
-        N = dims[0] + 2;
-        M = dims[1] + 2;
-        Z = dims[2] + 2;
-        int N_dims[] = {N, M, Z};
-        A_L = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
-        B_L = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
-        
-        /* output data */
-        B = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dims, mxSINGLE_CLASS, mxREAL));
-        
-        // copy A to the bigger A_L with boundaries
-        #pragma omp parallel for shared(A_L, A) private(i,j,k)
-        for (i=0; i < N; i++) {
-            for (j=0; j < M; j++) {
-                for (k=0; k < Z; k++) {
-                    if (((i > 0) && (i < N-1)) &&  ((j > 0) && (j < M-1)) &&  ((k > 0) && (k < Z-1))) {
-                        A_L[(N*M)*(k)+(i)*M+(j)] = A[(dims[0]*dims[1])*(k-1)+(i-1)*dims[1]+(j-1)];
-                    }}}}
-        
-        // Running CUDA kernel here for diffusivity
-        Diff4th_GPU_kernel(A_L, B_L, N, M, Z, (float)sigma, iter, (float)tau, lambda);
-        
-        // copy the processed B_L to a smaller B
-        #pragma omp parallel for shared(B_L, B) private(i,j,k)
-        for (i=0; i < N; i++) {
-            for (j=0; j < M; j++) {
-                for (k=0; k < Z; k++) {
-                    if (((i > 0) && (i < N-1)) &&  ((j > 0) && (j < M-1)) &&  ((k > 0) && (k < Z-1))) {
-                        B[(dims[0]*dims[1])*(k-1)+(i-1)*dims[1]+(j-1)] = B_L[(N*M)*(k)+(i)*M+(j)];
-                    }}}}
-    }
-}
\ No newline at end of file
diff --git a/Core/regularizers_GPU/NL_Regul/NLM_GPU.cpp b/Core/regularizers_GPU/NL_Regul/NLM_GPU.cpp
deleted file mode 100644
index 858b865..0000000
--- a/Core/regularizers_GPU/NL_Regul/NLM_GPU.cpp
+++ /dev/null
@@ -1,171 +0,0 @@
-#include "mex.h"
-#include <matrix.h>
-#include <math.h>
-#include <stdlib.h>
-#include <memory.h>
-#include <stdio.h>
-#include <iostream>
-#include "NLM_GPU_kernel.h"
-
-/* CUDA implementation of the patch-based (PB) regularization  for 2D and 3D images/volumes  
- * This method finds self-similar patches in data and performs one fixed point iteration to mimimize the PB penalty function
- * 
- * References: 1. Yang Z. & Jacob M. "Nonlocal Regularization of Inverse Problems"
- *             2. Kazantsev D. at. all "4D-CT reconstruction with unified spatial-temporal patch-based regularization"
- *
- * Input Parameters (mandatory):
- * 1. Image/volume (2D/3D)
- * 2. ratio of the searching window (e.g. 3 = (2*3+1) = 7 pixels window)
- * 3. ratio of the similarity window (e.g. 1 = (2*1+1) = 3 pixels window)
- * 4. h - parameter for the PB penalty function
- * 5. lambda - regularization parameter 
-
- * Output:
- * 1. regularized (denoised) Image/volume (N x N x N)
- *
- * In matlab check what kind of GPU you have with "gpuDevice" command,
- * then set your ComputeCapability, here I use -arch compute_35
- *
- * Quick 2D denoising example in Matlab:   
-   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-   ImDen = NLM_GPU(single(u0), 3, 2, 0.15, 1);
- 
- * Linux/Matlab compilation:
- * compile in terminal: nvcc -Xcompiler -fPIC -shared -o NLM_GPU_kernel.o NLM_GPU_kernel.cu
- * then compile in Matlab: mex -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart NLM_GPU.cpp NLM_GPU_kernel.o
- *
- * D. Kazantsev 
- * 2014-17
- * Harwell/Manchester UK
- */
-
-float pad_crop(float *A, float *Ap, int OldSizeX, int OldSizeY,  int OldSizeZ, int NewSizeX, int NewSizeY, int NewSizeZ, int padXY, int switchpad_crop);
-
-void mexFunction(
-        int nlhs, mxArray *plhs[],
-        int nrhs, const mxArray *prhs[])
-{
-    int N, M, Z, i_n, j_n, k_n, numdims, SearchW, SimilW, SearchW_real, padXY, newsizeX, newsizeY, newsizeZ, switchpad_crop, count, SearchW_full, SimilW_full;
-    const int  *dims;
-    float *A, *B=NULL, *Ap=NULL, *Bp=NULL, *Eucl_Vec, h, h2, lambda, val, denh2;
-    
-    numdims = mxGetNumberOfDimensions(prhs[0]);
-    dims = mxGetDimensions(prhs[0]);
-    
-    N = dims[0];
-    M = dims[1];
-    Z = dims[2];
-    
-    if ((numdims < 2) || (numdims > 3)) {mexErrMsgTxt("The input should be 2D image or 3D volume");}
-    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input in single precision is required"); }
-    
-    if(nrhs != 5) mexErrMsgTxt("Five inputs reqired: Image(2D,3D), SearchW, SimilW, Threshold, Regularization parameter");
-    
-    /*Handling inputs*/
-    A  = (float *) mxGetData(prhs[0]);    /* the image to regularize/filter */
-    SearchW_real  = (int) mxGetScalar(prhs[1]); /* the searching window ratio */
-    SimilW =  (int) mxGetScalar(prhs[2]);  /* the similarity window ratio */
-    h =  (float) mxGetScalar(prhs[3]);  /* parameter for the PB filtering function */
-    lambda = (float) mxGetScalar(prhs[4]);
-    
-    if (h <= 0) mexErrMsgTxt("Parmeter for the PB penalty function should be > 0");
-      
-    SearchW = SearchW_real + 2*SimilW;
-    
-    SearchW_full = 2*SearchW + 1; /* the full searching window  size */
-    SimilW_full = 2*SimilW + 1;   /* the full similarity window  size */
-    h2 = h*h;
-    
-    padXY = SearchW + 2*SimilW; /* padding sizes */
-    newsizeX = N + 2*(padXY); /* the X size of the padded array */
-    newsizeY = M + 2*(padXY); /* the Y size of the padded array */
-    newsizeZ = Z + 2*(padXY); /* the Z size of the padded array */
-    int N_dims[] = {newsizeX, newsizeY, newsizeZ};
-    
-    /******************************2D case ****************************/
-    if (numdims == 2) {
-        /*Handling output*/
-        B = (float*)mxGetData(plhs[0] = mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
-        /*allocating memory for the padded arrays */
-        Ap = (float*)mxGetData(mxCreateNumericMatrix(newsizeX, newsizeY, mxSINGLE_CLASS, mxREAL));
-        Bp = (float*)mxGetData(mxCreateNumericMatrix(newsizeX, newsizeY, mxSINGLE_CLASS, mxREAL));
-        Eucl_Vec = (float*)mxGetData(mxCreateNumericMatrix(SimilW_full*SimilW_full, 1, mxSINGLE_CLASS, mxREAL));
-        
-        /*Gaussian kernel */
-        count = 0;
-        for(i_n=-SimilW; i_n<=SimilW; i_n++) {
-            for(j_n=-SimilW; j_n<=SimilW; j_n++) {
-                val = (float)(i_n*i_n + j_n*j_n)/(2*SimilW*SimilW);
-                Eucl_Vec[count] = exp(-val);
-                count = count + 1;
-            }} /*main neighb loop */
-        
-        /**************************************************************************/
-        /*Perform padding of image A to the size of [newsizeX * newsizeY] */
-        switchpad_crop = 0; /*padding*/
-        pad_crop(A, Ap, M, N, 0, newsizeY, newsizeX, 0, padXY, switchpad_crop);
-        
-        /* Do PB regularization with the padded array  */
-        NLM_GPU_kernel(Ap, Bp, Eucl_Vec, newsizeY, newsizeX, 0, numdims, SearchW, SimilW, SearchW_real, (float)h2, (float)lambda);
-        
-        switchpad_crop = 1; /*cropping*/
-        pad_crop(Bp, B, M, N, 0, newsizeY, newsizeX, 0, padXY, switchpad_crop);
-    }
-    else
-    {
-        /******************************3D case ****************************/
-        /*Handling output*/
-        B = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dims, mxSINGLE_CLASS, mxREAL));
-        /*allocating memory for the padded arrays */
-        Ap = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
-        Bp = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
-        Eucl_Vec = (float*)mxGetData(mxCreateNumericMatrix(SimilW_full*SimilW_full*SimilW_full, 1, mxSINGLE_CLASS, mxREAL));
-        
-        /*Gaussian kernel */
-        count = 0;
-        for(i_n=-SimilW; i_n<=SimilW; i_n++) {
-            for(j_n=-SimilW; j_n<=SimilW; j_n++) {
-                for(k_n=-SimilW; k_n<=SimilW; k_n++) {
-                    val = (float)(i_n*i_n + j_n*j_n + k_n*k_n)/(2*SimilW*SimilW*SimilW);
-                    Eucl_Vec[count] = exp(-val);
-                    count = count + 1;
-                }}} /*main neighb loop */
-        /**************************************************************************/
-        /*Perform padding of image A to the size of [newsizeX * newsizeY * newsizeZ] */
-        switchpad_crop = 0; /*padding*/
-        pad_crop(A, Ap, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
-        
-        /* Do PB regularization with the padded array  */
-        NLM_GPU_kernel(Ap, Bp, Eucl_Vec, newsizeY, newsizeX, newsizeZ, numdims, SearchW, SimilW, SearchW_real, (float)h2, (float)lambda);
-        
-        switchpad_crop = 1; /*cropping*/
-        pad_crop(Bp, B, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
-    } /*end else ndims*/
-}
-
-float pad_crop(float *A, float *Ap, int OldSizeX, int OldSizeY, int OldSizeZ, int NewSizeX, int NewSizeY, int NewSizeZ, int padXY, int switchpad_crop)
-{
-    /* padding-cropping function */
-    int i,j,k;    
-    if (NewSizeZ > 1) {    
-           for (i=0; i < NewSizeX; i++) {
-            for (j=0; j < NewSizeY; j++) {
-              for (k=0; k < NewSizeZ; k++) {
-                if (((i >= padXY) && (i < NewSizeX-padXY)) &&  ((j >= padXY) && (j < NewSizeY-padXY)) &&  ((k >= padXY) && (k < NewSizeZ-padXY))) {
-                    if (switchpad_crop == 0)  Ap[NewSizeX*NewSizeY*k + i*NewSizeY+j] = A[OldSizeX*OldSizeY*(k - padXY) + (i-padXY)*(OldSizeY)+(j-padXY)];
-                    else  Ap[OldSizeX*OldSizeY*(k - padXY) + (i-padXY)*(OldSizeY)+(j-padXY)] = A[NewSizeX*NewSizeY*k + i*NewSizeY+j];
-                }
-            }}}   
-    }
-    else {
-        for (i=0; i < NewSizeX; i++) {
-            for (j=0; j < NewSizeY; j++) {
-                if (((i >= padXY) && (i < NewSizeX-padXY)) &&  ((j >= padXY) && (j < NewSizeY-padXY))) {
-                    if (switchpad_crop == 0)  Ap[i*NewSizeY+j] = A[(i-padXY)*(OldSizeY)+(j-padXY)];
-                    else  Ap[(i-padXY)*(OldSizeY)+(j-padXY)] = A[i*NewSizeY+j];
-                }
-            }}
-    }
-    return *Ap;
-}
\ No newline at end of file
diff --git a/Wrappers/Matlab/compile_mex.m b/Wrappers/Matlab/compile_mex.m
deleted file mode 100644
index 66c05da..0000000
--- a/Wrappers/Matlab/compile_mex.m
+++ /dev/null
@@ -1,11 +0,0 @@
-% compile mex's in Matlab once
-cd regularizers_CPU/
-
-mex LLT_model.c LLT_model_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
-mex FGP_TV.c FGP_TV_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
-mex SplitBregman_TV.c SplitBregman_TV_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
-mex TGV_PD.c TGV_PD_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
-mex PatchBased_Regul.c PatchBased_Regul_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
-
-cd ../../
-cd demos
diff --git a/demos/Demo_Phantom3D_Cone.m b/Wrappers/Matlab/demos/Demo_Phantom3D_Cone.m
index a8f2c92..a8f2c92 100644
--- a/demos/Demo_Phantom3D_Cone.m
+++ b/Wrappers/Matlab/demos/Demo_Phantom3D_Cone.m
diff --git a/demos/Demo_Phantom3D_Parallel.m b/Wrappers/Matlab/demos/Demo_Phantom3D_Parallel.m
index 4219bd1..4219bd1 100644
--- a/demos/Demo_Phantom3D_Parallel.m
+++ b/Wrappers/Matlab/demos/Demo_Phantom3D_Parallel.m
diff --git a/demos/Demo_RealData3D_Parallel.m b/Wrappers/Matlab/demos/Demo_RealData3D_Parallel.m
index f82e0b0..f82e0b0 100644
--- a/demos/Demo_RealData3D_Parallel.m
+++ b/Wrappers/Matlab/demos/Demo_RealData3D_Parallel.m
diff --git a/demos/exportDemoRD2Data.m b/Wrappers/Matlab/demos/exportDemoRD2Data.m
index 028353b..028353b 100644
--- a/demos/exportDemoRD2Data.m
+++ b/Wrappers/Matlab/demos/exportDemoRD2Data.m
diff --git a/main_func/compile_mex.m b/Wrappers/Matlab/mex_compile/compile_mex.m
index 1353859..1353859 100644
--- a/main_func/compile_mex.m
+++ b/Wrappers/Matlab/mex_compile/compile_mex.m
diff --git a/Core/regularizers_CPU/FGP_TV.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV.c
index 30cea1a..30cea1a 100644
--- a/Core/regularizers_CPU/FGP_TV.c
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV.c
diff --git a/main_func/regularizers_CPU/FGP_TV_core.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV_core.c
index 03cd445..03cd445 100644
--- a/main_func/regularizers_CPU/FGP_TV_core.c
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV_core.c
diff --git a/main_func/regularizers_CPU/FGP_TV_core.h b/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV_core.h
index 6430bf2..6430bf2 100644
--- a/main_func/regularizers_CPU/FGP_TV_core.h
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/FGP_TV_core.h
diff --git a/Core/regularizers_CPU/LLT_model.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model.c
index 0b07b47..0b07b47 100644
--- a/Core/regularizers_CPU/LLT_model.c
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model.c
diff --git a/main_func/regularizers_CPU/LLT_model_core.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model_core.c
index 3a853d2..3a853d2 100644
--- a/main_func/regularizers_CPU/LLT_model_core.c
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model_core.c
diff --git a/main_func/regularizers_CPU/LLT_model_core.h b/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model_core.h
index 13fce5a..13fce5a 100644
--- a/main_func/regularizers_CPU/LLT_model_core.h
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/LLT_model_core.h
diff --git a/main_func/regularizers_CPU/PatchBased_Regul.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul.c
index 9c925df..9c925df 100644
--- a/main_func/regularizers_CPU/PatchBased_Regul.c
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul.c
diff --git a/main_func/regularizers_CPU/PatchBased_Regul_core.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul_core.c
index acfb464..acfb464 100644
--- a/main_func/regularizers_CPU/PatchBased_Regul_core.c
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul_core.c
diff --git a/main_func/regularizers_CPU/PatchBased_Regul_core.h b/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul_core.h
index d4a8a46..d4a8a46 100644
--- a/main_func/regularizers_CPU/PatchBased_Regul_core.h
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/PatchBased_Regul_core.h
diff --git a/Core/regularizers_CPU/SplitBregman_TV.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV.c
index 38f6a9d..38f6a9d 100644
--- a/Core/regularizers_CPU/SplitBregman_TV.c
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV.c
diff --git a/main_func/regularizers_CPU/SplitBregman_TV_core.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV_core.c
index 4109a4b..4109a4b 100644
--- a/main_func/regularizers_CPU/SplitBregman_TV_core.c
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV_core.c
diff --git a/main_func/regularizers_CPU/SplitBregman_TV_core.h b/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV_core.h
index 6ed3ff9..6ed3ff9 100644
--- a/main_func/regularizers_CPU/SplitBregman_TV_core.h
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/SplitBregman_TV_core.h
diff --git a/Core/regularizers_CPU/TGV_PD.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD.c
index c9cb440..c9cb440 100644
--- a/Core/regularizers_CPU/TGV_PD.c
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD.c
diff --git a/main_func/regularizers_CPU/TGV_PD_core.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD_core.c
index 4139d10..4139d10 100644
--- a/main_func/regularizers_CPU/TGV_PD_core.c
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD_core.c
diff --git a/main_func/regularizers_CPU/TGV_PD_core.h b/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD_core.h
index d5378df..d5378df 100644
--- a/main_func/regularizers_CPU/TGV_PD_core.h
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/TGV_PD_core.h
diff --git a/main_func/regularizers_CPU/utils.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/utils.c
index 0e83d2c..0e83d2c 100644
--- a/main_func/regularizers_CPU/utils.c
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/utils.c
diff --git a/main_func/regularizers_CPU/utils.h b/Wrappers/Matlab/mex_compile/regularizers_CPU/utils.h
index 53463a3..53463a3 100644
--- a/main_func/regularizers_CPU/utils.h
+++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/utils.h
diff --git a/main_func/regularizers_GPU/Diffus_HO/Diff4thHajiaboli_GPU.cpp b/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4thHajiaboli_GPU.cpp
index 5a8c7c0..5a8c7c0 100644
--- a/main_func/regularizers_GPU/Diffus_HO/Diff4thHajiaboli_GPU.cpp
+++ b/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4thHajiaboli_GPU.cpp
diff --git a/main_func/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.cu b/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.cu
index 178af00..178af00 100644
--- a/main_func/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.cu
+++ b/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.cu
diff --git a/main_func/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.h b/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.h
index cfbb45a..cfbb45a 100644
--- a/main_func/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.h
+++ b/Wrappers/Matlab/mex_compile/regularizers_GPU/Diffus_HO/Diff4th_GPU_kernel.h
diff --git a/main_func/regularizers_GPU/NL_Regul/NLM_GPU.cpp b/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU.cpp
index ff0cc90..ff0cc90 100644
--- a/main_func/regularizers_GPU/NL_Regul/NLM_GPU.cpp
+++ b/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU.cpp
diff --git a/main_func/regularizers_GPU/NL_Regul/NLM_GPU_kernel.cu b/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU_kernel.cu
index 17da3a8..17da3a8 100644
--- a/main_func/regularizers_GPU/NL_Regul/NLM_GPU_kernel.cu
+++ b/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU_kernel.cu
diff --git a/main_func/regularizers_GPU/NL_Regul/NLM_GPU_kernel.h b/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU_kernel.h
index bc9d4a3..bc9d4a3 100644
--- a/main_func/regularizers_GPU/NL_Regul/NLM_GPU_kernel.h
+++ b/Wrappers/Matlab/mex_compile/regularizers_GPU/NL_Regul/NLM_GPU_kernel.h
diff --git a/supp/RMSE.m b/Wrappers/Matlab/supp/RMSE.m
index 002f776..002f776 100644
--- a/supp/RMSE.m
+++ b/Wrappers/Matlab/supp/RMSE.m
diff --git a/supp/my_red_yellowMAP.mat b/Wrappers/Matlab/supp/my_red_yellowMAP.mat
index c2a5b87..c2a5b87 100644
--- a/supp/my_red_yellowMAP.mat
+++ b/Wrappers/Matlab/supp/my_red_yellowMAP.mat
diff --git a/supp/sino_add_artifacts.m b/Wrappers/Matlab/supp/sino_add_artifacts.m
index f601914..f601914 100644
--- a/supp/sino_add_artifacts.m
+++ b/Wrappers/Matlab/supp/sino_add_artifacts.m
diff --git a/Wrappers/Matlab/studentst.m b/Wrappers/Matlab/supp/studentst.m
index 99fed1e..99fed1e 100644
--- a/Wrappers/Matlab/studentst.m
+++ b/Wrappers/Matlab/supp/studentst.m
diff --git a/supp/zing_rings_add.m b/Wrappers/Matlab/supp/zing_rings_add.m
index d197b1f..d197b1f 100644
--- a/supp/zing_rings_add.m
+++ b/Wrappers/Matlab/supp/zing_rings_add.m
diff --git a/demos/DendrData.h5 b/demos/DendrData.h5
deleted file mode 100644
index f048268..0000000
--- a/demos/DendrData.h5
+++ /dev/null
diff --git a/main_func/FISTA_REC.m b/main_func/FISTA_REC.m
deleted file mode 100644
index d717a03..0000000
--- a/main_func/FISTA_REC.m
+++ /dev/null
@@ -1,704 +0,0 @@
-function [X,  output] = FISTA_REC(params)
-
-% <<<< FISTA-based reconstruction routine using ASTRA-toolbox >>>>
-% This code solves regularised PWLS problem using FISTA approach.
-% The code contains multiple regularisation penalties as well as it can be
-% accelerated by using ordered-subset version. Various projection
-% geometries supported.
-
-% DISCLAIMER
-% It is recommended to use ASTRA version 1.8 or later in order to avoid
-% crashing due to GPU memory overflow for big datasets
-
-% ___Input___:
-% params.[] file:
-%----------------General Parameters------------------------
-%       - .proj_geom (geometry of the projector) [required]
-%       - .vol_geom (geometry of the reconstructed object) [required]
-%       - .sino (2D or 3D sinogram) [required]
-%       - .iterFISTA (iterations for the main loop, default 40)
-%       - .L_const (Lipschitz constant, default Power method)                                                                                                    )
-%       - .X_ideal (ideal image, if given)
-%       - .weights (statisitcal weights for the PWLS model, size of the sinogram)
-%       - .fidelity (use 'studentt' fidelity)
-%       - .ROI (Region-of-interest, only if X_ideal is given)
-%       - .initialize (a 'warm start' using SIRT method from ASTRA)
-%----------------Regularization choices------------------------
-%       1 .Regul_Lambda_FGPTV (FGP-TV regularization parameter)
-%       2 .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter)
-%       3 .Regul_LambdaLLT (Higher order LLT regularization parameter)
-%          3.1 .Regul_tauLLT (time step parameter for LLT (HO) term)
-%       4 .Regul_LambdaPatchBased_CPU (Patch-based nonlocal regularization parameter)
-%          4.1  .Regul_PB_SearchW (ratio of the searching window (e.g. 3 = (2*3+1) = 7 pixels window))
-%          4.2  .Regul_PB_SimilW (ratio of the similarity window (e.g. 1 = (2*1+1) = 3 pixels window))
-%          4.3  .Regul_PB_h (PB penalty function threshold)
-%       5 .Regul_LambdaPatchBased_GPU (Patch-based nonlocal regularization parameter)
-%          5.1  .Regul_PB_SearchW (ratio of the searching window (e.g. 3 = (2*3+1) = 7 pixels window))
-%          5.2  .Regul_PB_SimilW (ratio of the similarity window (e.g. 1 = (2*1+1) = 3 pixels window))
-%          5.3  .Regul_PB_h (PB penalty function threshold)
-%       6 .Regul_LambdaDiffHO (Higher-Order Diffusion regularization parameter)
-%          6.1  .Regul_DiffHO_EdgePar (edge-preserving noise related parameter)
-%       7 .Regul_LambdaTGV (Total Generalized variation regularization parameter)
-%       - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04)
-%       - .Regul_Iterations (iterations for the selected penalty, default 25)
-%       - .Regul_Dimension ('2D' or '3D' way to apply regularization, '3D' is the default)
-%----------------Ring removal------------------------
-%       - .Ring_LambdaR_L1 (regularization parameter for L1-ring minimization, if lambdaR_L1 > 0 then switch on ring removal)
-%       - .Ring_Alpha (larger values can accelerate convergence but check stability, default 1)
-%----------------Visualization parameters------------------------
-%       - .show (visualize reconstruction 1/0, (0 default))
-%       - .maxvalplot (maximum value to use for imshow[0 maxvalplot])
-%       - .slice (for 3D volumes - slice number to imshow)
-% ___Output___:
-% 1. X - reconstructed image/volume
-% 2. output - a structure with
-%    - .Resid_error - residual error (if X_ideal is given)
-%    - .objective: value of the objective function
-%    - .L_const: Lipshitz constant to avoid recalculations
-
-% References:
-% 1. "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse
-% Problems" by A. Beck and M Teboulle
-% 2. "Ring artifacts correction in compressed sensing..." by P. Paleo
-% 3. "A novel tomographic reconstruction method based on the robust
-% Student's t function for suppressing data outliers" D. Kazantsev et.al.
-% D. Kazantsev, 2016-17
-
-% Dealing with input parameters
-if (isfield(params,'proj_geom') == 0)
-    error('%s \n', 'Please provide ASTRA projection geometry - proj_geom');
-else
-    proj_geom = params.proj_geom;
-end
-if (isfield(params,'vol_geom') == 0)
-    error('%s \n', 'Please provide ASTRA object geometry - vol_geom');
-else
-    vol_geom = params.vol_geom;
-end
-N = params.vol_geom.GridColCount;
-if (isfield(params,'sino'))
-    sino = params.sino;
-    [Detectors, anglesNumb, SlicesZ] = size(sino);
-    fprintf('%s %i %s %i %s %i %s \n', 'Sinogram has a dimension of', Detectors, 'detectors;', anglesNumb, 'projections;', SlicesZ, 'vertical slices.');
-else
-    error('%s \n', 'Please provide a sinogram');
-end
-if (isfield(params,'iterFISTA'))
-    iterFISTA = params.iterFISTA;
-else
-    iterFISTA = 40;
-end
-if (isfield(params,'weights'))
-    weights = params.weights;
-else
-    weights = ones(size(sino));
-end
-if (isfield(params,'fidelity'))
-    studentt = 0;
-    if (strcmp(params.fidelity,'studentt') == 1)
-        studentt = 1;
-    end
-else
-    studentt = 0;
-end
-if (isfield(params,'L_const'))
-    L_const = params.L_const;
-else
-    % using Power method (PM) to establish L constant
-    fprintf('%s %s %s \n', 'Calculating Lipshitz constant for',proj_geom.type, 'beam geometry...');
-    if (strcmp(proj_geom.type,'parallel') || strcmp(proj_geom.type,'fanflat') || strcmp(proj_geom.type,'fanflat_vec'))
-        % for 2D geometry we can do just one selected slice
-        niter = 15; % number of iteration for the PM
-        x1 = rand(N,N,1);
-        sqweight = sqrt(weights(:,:,1));
-        [sino_id, y] = astra_create_sino_cuda(x1, proj_geom, vol_geom);
-        y = sqweight.*y';
-        astra_mex_data2d('delete', sino_id);
-        for i = 1:niter
-            [x1] = astra_create_backprojection_cuda((sqweight.*y)', proj_geom, vol_geom);
-            s = norm(x1(:));
-            x1 = x1./s;
-            [sino_id, y] = astra_create_sino_cuda(x1, proj_geom, vol_geom);
-            y = sqweight.*y';
-            astra_mex_data2d('delete', sino_id);
-        end
-    elseif (strcmp(proj_geom.type,'cone') || strcmp(proj_geom.type,'parallel3d') || strcmp(proj_geom.type,'parallel3d_vec') || strcmp(proj_geom.type,'cone_vec'))
-        % 3D geometry
-        niter = 8; % number of iteration for PM
-        x1 = rand(N,N,SlicesZ);
-        sqweight = sqrt(weights);
-        [sino_id, y] = astra_create_sino3d_cuda(x1, proj_geom, vol_geom);
-        y = sqweight.*y;
-        astra_mex_data3d('delete', sino_id);
-        
-        for i = 1:niter
-            [id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geom, vol_geom);
-            s = norm(x1(:));
-            x1 = x1/s;
-            [sino_id, y] = astra_create_sino3d_cuda(x1, proj_geom, vol_geom);
-            y = sqweight.*y;
-            astra_mex_data3d('delete', sino_id);
-            astra_mex_data3d('delete', id);
-        end
-        clear x1
-    else
-        error('%s \n', 'No suitable geometry has been found!');
-    end
-    L_const = s;
-end
-if (isfield(params,'X_ideal'))
-    X_ideal = params.X_ideal;
-else
-    X_ideal = 'none';
-end
-if (isfield(params,'ROI'))
-    ROI = params.ROI;
-else
-    ROI = find(X_ideal>=0.0);
-end
-if (isfield(params,'Regul_Lambda_FGPTV'))
-    lambdaFGP_TV = params.Regul_Lambda_FGPTV;
-else
-    lambdaFGP_TV = 0;
-end
-if (isfield(params,'Regul_Lambda_SBTV'))
-    lambdaSB_TV = params.Regul_Lambda_SBTV;
-else
-    lambdaSB_TV = 0;
-end
-if (isfield(params,'Regul_tol'))
-    tol = params.Regul_tol;
-else
-    tol = 1.0e-05;
-end
-if (isfield(params,'Regul_Iterations'))
-    IterationsRegul = params.Regul_Iterations;
-else
-    IterationsRegul = 45;
-end
-if (isfield(params,'Regul_LambdaLLT'))
-    lambdaHO = params.Regul_LambdaLLT;
-else
-    lambdaHO = 0;
-end
-if (isfield(params,'Regul_iterHO'))
-    iterHO = params.Regul_iterHO;
-else
-    iterHO = 50;
-end
-if (isfield(params,'Regul_tauLLT'))
-    tauHO = params.Regul_tauLLT;
-else
-    tauHO = 0.0001;
-end
-if (isfield(params,'Regul_LambdaPatchBased_CPU'))
-    lambdaPB = params.Regul_LambdaPatchBased_CPU;
-else
-    lambdaPB = 0;
-end
-if (isfield(params,'Regul_LambdaPatchBased_GPU'))
-    lambdaPB_GPU = params.Regul_LambdaPatchBased_GPU;
-else
-    lambdaPB_GPU = 0;
-end
-if (isfield(params,'Regul_PB_SearchW'))
-    SearchW = params.Regul_PB_SearchW;
-else
-    SearchW = 3; % default
-end
-if (isfield(params,'Regul_PB_SimilW'))
-    SimilW = params.Regul_PB_SimilW;
-else
-    SimilW = 1; % default
-end
-if (isfield(params,'Regul_PB_h'))
-    h_PB = params.Regul_PB_h;
-else
-    h_PB = 0.1; % default
-end
-if (isfield(params,'Regul_LambdaDiffHO'))
-    LambdaDiff_HO = params.Regul_LambdaDiffHO;
-else
-    LambdaDiff_HO = 0;
-end
-if (isfield(params,'Regul_DiffHO_EdgePar'))
-    LambdaDiff_HO_EdgePar = params.Regul_DiffHO_EdgePar;
-else
-    LambdaDiff_HO_EdgePar = 0.01;
-end
-if (isfield(params,'Regul_LambdaTGV'))
-    LambdaTGV = params.Regul_LambdaTGV;
-else
-    LambdaTGV = 0;
-end
-if (isfield(params,'Ring_LambdaR_L1'))
-    lambdaR_L1 = params.Ring_LambdaR_L1;
-else
-    lambdaR_L1 = 0;
-end
-if (isfield(params,'Ring_Alpha'))
-    alpha_ring = params.Ring_Alpha; % higher values can accelerate ring removal procedure
-else
-    alpha_ring = 1;
-end
-if (isfield(params,'Regul_Dimension'))
-    Dimension = params.Regul_Dimension;
-    if ((strcmp('2D', Dimension) ~= 1) && (strcmp('3D', Dimension) ~= 1))
-        Dimension = '3D';
-    end
-else
-    Dimension = '3D';
-end
-if (isfield(params,'show'))
-    show = params.show;
-else
-    show = 0;
-end
-if (isfield(params,'maxvalplot'))
-    maxvalplot = params.maxvalplot;
-else
-    maxvalplot = 1;
-end
-if (isfield(params,'slice'))
-    slice = params.slice;
-else
-    slice = 1;
-end
-if (isfield(params,'initialize'))
-    X  = params.initialize;
-    if ((size(X,1) ~= N) || (size(X,2) ~= N) || (size(X,3) ~= SlicesZ))
-        error('%s \n', 'The initialized volume has different dimensions!');
-    end
-else
-    X = zeros(N,N,SlicesZ, 'single'); % storage for the solution
-end
-if (isfield(params,'subsets'))
-    % Ordered Subsets reorganisation of data and angles
-    subsets = params.subsets; % subsets number
-    angles = proj_geom.ProjectionAngles;
-    binEdges = linspace(min(angles),max(angles),subsets+1);
-    
-    % assign values to bins
-    [binsDiscr,~] = histc(angles, [binEdges(1:end-1) Inf]);
-    
-    % get rearranged subset indices
-    IndicesReorg = zeros(length(angles),1);
-    counterM = 0;
-    for ii = 1:max(binsDiscr(:))
-        counter = 0;
-        for jj = 1:subsets
-            curr_index = ii+jj-1 + counter;
-            if (binsDiscr(jj) >= ii)
-                counterM = counterM + 1;
-                IndicesReorg(counterM) = curr_index;
-            end
-            counter = (counter + binsDiscr(jj)) - 1;
-        end
-    end
-else
-    subsets = 0; % Classical FISTA
-end
-
-%----------------Reconstruction part------------------------
-Resid_error = zeros(iterFISTA,1); % errors vector (if the ground truth is given)
-objective = zeros(iterFISTA,1); % objective function values vector
-
-
-if (subsets == 0)
-    % Classical FISTA
-    t = 1;
-    X_t = X;
-    
-    r = zeros(Detectors,SlicesZ, 'single'); % 2D array (for 3D data) of sparse "ring" vectors
-    r_x = r; % another ring variable
-    residual = zeros(size(sino),'single');
-    
-    % Outer FISTA iterations loop
-    for i = 1:iterFISTA
-        
-        X_old = X;
-        t_old = t;
-        r_old = r;
-        
-        
-        if (strcmp(proj_geom.type,'parallel') || strcmp(proj_geom.type,'fanflat') || strcmp(proj_geom.type,'fanflat_vec'))
-            % if geometry is 2D use slice-by-slice projection-backprojection routine
-            sino_updt = zeros(size(sino),'single');
-            for kkk = 1:SlicesZ
-                [sino_id, sinoT] = astra_create_sino_cuda(X_t(:,:,kkk), proj_geom, vol_geom);
-                sino_updt(:,:,kkk) = sinoT';
-                astra_mex_data2d('delete', sino_id);
-            end
-        else
-            % for 3D geometry (watch the GPU memory overflow in earlier ASTRA versions < 1.8)
-            [sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom);
-            astra_mex_data3d('delete', sino_id);
-        end
-        
-        if (lambdaR_L1 > 0)
-            % the ring removal part (Group-Huber fidelity)
-            for kkk = 1:anglesNumb
-                residual(:,kkk,:) =  squeeze(weights(:,kkk,:)).*(squeeze(sino_updt(:,kkk,:)) - (squeeze(sino(:,kkk,:)) - alpha_ring.*r_x));
-            end
-            vec = sum(residual,2);
-            if (SlicesZ > 1)
-                vec = squeeze(vec(:,1,:));
-            end
-            r = r_x - (1./L_const).*vec;
-            objective(i) = (0.5*sum(residual(:).^2)); % for the objective function output
-        elseif (studentt > 0)
-            % artifacts removal with Students t penalty
-            residual = weights.*(sino_updt - sino);
-            for kkk = 1:SlicesZ
-                res_vec = reshape(residual(:,:,kkk), Detectors*anglesNumb, 1); % 1D vectorized sinogram
-                %s = 100;
-                %gr = (2)*res_vec./(s*2 + conj(res_vec).*res_vec);
-                [ff, gr] = studentst(res_vec, 1);
-                residual(:,:,kkk) = reshape(gr, Detectors, anglesNumb);
-            end
-            objective(i) = ff; % for the objective function output
-        else
-            % no ring removal (LS model)
-            residual = weights.*(sino_updt - sino);
-            objective(i) = 0.5*norm(residual(:)); % for the objective function output
-        end
-        
-        % if the geometry is 2D use slice-by-slice projection-backprojection routine
-        if (strcmp(proj_geom.type,'parallel') || strcmp(proj_geom.type,'fanflat') || strcmp(proj_geom.type,'fanflat_vec'))
-            x_temp = zeros(size(X),'single');
-            for kkk = 1:SlicesZ
-                [x_temp(:,:,kkk)] = astra_create_backprojection_cuda(squeeze(residual(:,:,kkk))', proj_geom, vol_geom);
-            end
-        else
-            [id, x_temp] = astra_create_backprojection3d_cuda(residual, proj_geom, vol_geom);
-            astra_mex_data3d('delete', id);
-        end
-        X = X_t - (1/L_const).*x_temp;
-        
-        % ----------------Regularization part------------------------%
-        if (lambdaFGP_TV > 0)
-            % FGP-TV regularization
-            if ((strcmp('2D', Dimension) == 1))
-                % 2D regularization
-                for kkk = 1:SlicesZ
-                    [X(:,:,kkk), f_val] = FGP_TV(single(X(:,:,kkk)), lambdaFGP_TV/L_const, IterationsRegul, tol, 'iso');
-                end
-            else
-                % 3D regularization
-                [X, f_val] = FGP_TV(single(X), lambdaFGP_TV/L_const, IterationsRegul, tol, 'iso');
-            end
-            objective(i) = (objective(i) + f_val)./(Detectors*anglesNumb*SlicesZ);
-        end
-        if (lambdaSB_TV > 0)
-            % Split Bregman regularization
-            if ((strcmp('2D', Dimension) == 1))
-                % 2D regularization
-                for kkk = 1:SlicesZ
-                    X(:,:,kkk) = SplitBregman_TV(single(X(:,:,kkk)), lambdaSB_TV/L_const, IterationsRegul, tol);  % (more memory efficent)
-                end
-            else
-                % 3D regularization
-                X = SplitBregman_TV(single(X), lambdaSB_TV/L_const, IterationsRegul, tol);  % (more memory efficent)
-            end
-        end
-        if (lambdaHO > 0)
-            % Higher Order (LLT) regularization
-            X2 = zeros(N,N,SlicesZ,'single');
-            if ((strcmp('2D', Dimension) == 1))
-                % 2D regularization
-                for kkk = 1:SlicesZ
-                    X2(:,:,kkk) = LLT_model(single(X(:,:,kkk)), lambdaHO/L_const, tauHO, iterHO, 3.0e-05, 0);
-                end
-            else
-                % 3D regularization
-                X2 = LLT_model(single(X), lambdaHO/L_const, tauHO, iterHO, 3.0e-05, 0);
-            end
-            X = 0.5.*(X + X2); % averaged combination of two solutions
-            
-        end
-        if (lambdaPB > 0)
-            % Patch-Based regularization (can be very slow on CPU)
-            if ((strcmp('2D', Dimension) == 1))
-                % 2D regularization
-                for kkk = 1:SlicesZ
-                    X(:,:,kkk) = PatchBased_Regul(single(X(:,:,kkk)), SearchW, SimilW, h_PB, lambdaPB/L_const);
-                end
-            else
-                X = PatchBased_Regul(single(X), SearchW, SimilW, h_PB, lambdaPB/L_const);
-            end
-        end
-        if (lambdaPB_GPU > 0)
-            % Patch-Based regularization (GPU CUDA implementation)
-            if ((strcmp('2D', Dimension) == 1))
-                % 2D regularization
-                for kkk = 1:SlicesZ
-                    X(:,:,kkk) = NLM_GPU(single(X(:,:,kkk)), SearchW, SimilW, h_PB, lambdaPB_GPU/L_const);
-                end
-            else
-                X = NLM_GPU(single(X), SearchW, SimilW, h_PB, lambdaPB_GPU/L_const);
-            end
-        end
-        if (LambdaDiff_HO > 0)
-            % Higher-order diffusion penalty (GPU CUDA implementation)
-            if ((strcmp('2D', Dimension) == 1))
-                % 2D regularization
-                for kkk = 1:SlicesZ
-                    X(:,:,kkk) = Diff4thHajiaboli_GPU(single(X(:,:,kkk)), LambdaDiff_HO_EdgePar, LambdaDiff_HO/L_const, IterationsRegul);
-                end
-            else
-                X = Diff4thHajiaboli_GPU(X, LambdaDiff_HO_EdgePar, LambdaDiff_HO/L_const, IterationsRegul);
-            end
-        end
-        if (LambdaTGV > 0)
-            % Total Generalized variation (currently only 2D)
-            lamTGV1 = 1.1; % smoothing trade-off parameters, see Pock's paper
-            lamTGV2 = 0.8; % second-order term
-            for kkk = 1:SlicesZ
-                X(:,:,kkk) = TGV_PD(single(X(:,:,kkk)), LambdaTGV/L_const, lamTGV1, lamTGV2, IterationsRegul);
-            end
-        end
-        
-        if (lambdaR_L1 > 0)
-            r =  max(abs(r)-lambdaR_L1, 0).*sign(r); % soft-thresholding operator for ring vector
-        end
-        
-        t = (1 + sqrt(1 + 4*t^2))/2; % updating t
-        X_t = X + ((t_old-1)/t).*(X - X_old); % updating X
-        
-        if (lambdaR_L1 > 0)
-            r_x = r + ((t_old-1)/t).*(r - r_old); % updating r
-        end
-        
-        if (show == 1)
-            figure(10); imshow(X(:,:,slice), [0 maxvalplot]);
-            if (lambdaR_L1 > 0)
-                figure(11); plot(r); title('Rings offset vector')
-            end
-            pause(0.01);
-        end
-        if (strcmp(X_ideal, 'none' ) == 0)
-            Resid_error(i) = RMSE(X(ROI), X_ideal(ROI));
-            fprintf('%s %i %s %s %.4f  %s %s %f \n', 'Iteration Number:', i, '|', 'Error RMSE:', Resid_error(i), '|', 'Objective:', objective(i));
-        else
-            fprintf('%s %i  %s %s %f \n', 'Iteration Number:', i, '|', 'Objective:', objective(i));
-        end
-    end
-else
-    % Ordered Subsets (OS) FISTA reconstruction routine (normally one order of magnitude faster than the classical version)
-    t = 1;
-    X_t = X;
-    proj_geomSUB = proj_geom;
-    
-    r = zeros(Detectors,SlicesZ, 'single'); % 2D array (for 3D data) of sparse "ring" vectors
-    r_x = r; % another ring variable
-    residual2 = zeros(size(sino),'single');
-    sino_updt_FULL = zeros(size(sino),'single');
-    
-    
-    % Outer FISTA iterations loop
-    for i = 1:iterFISTA
-        
-        if ((i > 1) && (lambdaR_L1 > 0))
-            % in order to make Group-Huber fidelity work with ordered subsets
-            % we still need to work with full sinogram
-            
-            % the offset variable must be calculated for the whole
-            % updated sinogram - sino_updt_FULL
-            for kkk = 1:anglesNumb
-                residual2(:,kkk,:) = squeeze(weights(:,kkk,:)).*(squeeze(sino_updt_FULL(:,kkk,:)) - (squeeze(sino(:,kkk,:)) - alpha_ring.*r_x));
-            end
-            
-            r_old = r;
-            vec = sum(residual2,2);
-            if (SlicesZ > 1)
-                vec = squeeze(vec(:,1,:));
-            end
-            r = r_x - (1./L_const).*vec; % update ring variable
-        end
-                
-       % subsets loop
-        counterInd = 1;        
-        for ss = 1:subsets
-            X_old = X;
-            t_old = t;
-            
-            numProjSub = binsDiscr(ss); % the number of projections per subset
-            sino_updt_Sub = zeros(Detectors, numProjSub, SlicesZ,'single');
-            CurrSubIndeces = IndicesReorg(counterInd:(counterInd + numProjSub - 1)); % extract indeces attached to the subset
-            proj_geomSUB.ProjectionAngles = angles(CurrSubIndeces);
-            
-            if (strcmp(proj_geom.type,'parallel') || strcmp(proj_geom.type,'fanflat') || strcmp(proj_geom.type,'fanflat_vec'))
-                % if geometry is 2D use slice-by-slice projection-backprojection routine
-                for kkk = 1:SlicesZ
-                    [sino_id, sinoT] = astra_create_sino_cuda(X_t(:,:,kkk), proj_geomSUB, vol_geom);
-                    sino_updt_Sub(:,:,kkk) = sinoT';
-                    astra_mex_data2d('delete', sino_id);
-                end
-            else
-                % for 3D geometry (watch the GPU memory overflow in earlier ASTRA versions < 1.8)
-                [sino_id, sino_updt_Sub] = astra_create_sino3d_cuda(X_t, proj_geomSUB, vol_geom);
-                astra_mex_data3d('delete', sino_id);
-            end
-            
-            if (lambdaR_L1 > 0)
-                % Group-Huber fidelity (ring removal)
-                residualSub = zeros(Detectors, numProjSub, SlicesZ,'single'); % residual for a chosen subset
-                for kkk = 1:numProjSub
-                    indC = CurrSubIndeces(kkk);
-                    residualSub(:,kkk,:) =  squeeze(weights(:,indC,:)).*(squeeze(sino_updt_Sub(:,kkk,:)) - (squeeze(sino(:,indC,:)) - alpha_ring.*r_x));
-                    sino_updt_FULL(:,indC,:) = squeeze(sino_updt_Sub(:,kkk,:)); % filling the full sinogram
-                end
-                
-            elseif (studentt > 0)
-                % student t data fidelity
-                
-                % artifacts removal with Students t penalty
-                residualSub = squeeze(weights(:,CurrSubIndeces,:)).*(sino_updt_Sub - squeeze(sino(:,CurrSubIndeces,:)));
-                
-                for kkk = 1:SlicesZ
-                    res_vec = reshape(residualSub(:,:,kkk), Detectors*numProjSub, 1); % 1D vectorized sinogram
-                    %s = 100;
-                    %gr = (2)*res_vec./(s*2 + conj(res_vec).*res_vec);
-                    [ff, gr] = studentst(res_vec, 1);
-                    residualSub(:,:,kkk) = reshape(gr, Detectors, numProjSub);
-                end
-                objective(i) = ff; % for the objective function output
-            else
-                % PWLS model                
-                residualSub = squeeze(weights(:,CurrSubIndeces,:)).*(sino_updt_Sub - squeeze(sino(:,CurrSubIndeces,:)));
-                objective(i) = 0.5*norm(residualSub(:)); % for the objective function output
-            end
-            
-            % perform backprojection of a subset
-            if (strcmp(proj_geom.type,'parallel') || strcmp(proj_geom.type,'fanflat') || strcmp(proj_geom.type,'fanflat_vec'))
-                % if geometry is 2D use slice-by-slice projection-backprojection routine
-                x_temp = zeros(size(X),'single');
-                for kkk = 1:SlicesZ
-                    [x_temp(:,:,kkk)] = astra_create_backprojection_cuda(squeeze(residualSub(:,:,kkk))', proj_geomSUB, vol_geom);
-                end
-            else
-                [id, x_temp] = astra_create_backprojection3d_cuda(residualSub, proj_geomSUB, vol_geom);
-                astra_mex_data3d('delete', id);
-            end
-            
-            X = X_t - (1/L_const).*x_temp;
-            
-            % ----------------Regularization part------------------------%
-            if (lambdaFGP_TV > 0)
-                % FGP-TV regularization
-                if ((strcmp('2D', Dimension) == 1))
-                    % 2D regularization
-                    for kkk = 1:SlicesZ
-                        [X(:,:,kkk), f_val] = FGP_TV(single(X(:,:,kkk)), lambdaFGP_TV/(subsets*L_const), IterationsRegul, tol, 'iso');
-                    end
-                else
-                    % 3D regularization
-                    [X, f_val] = FGP_TV(single(X), lambdaFGP_TV/(subsets*L_const), IterationsRegul, tol, 'iso');
-                end
-                objective(i) = objective(i) + f_val;
-            end
-            if (lambdaSB_TV > 0)
-                % Split Bregman regularization
-                if ((strcmp('2D', Dimension) == 1))
-                    % 2D regularization
-                    for kkk = 1:SlicesZ
-                        X(:,:,kkk) = SplitBregman_TV(single(X(:,:,kkk)), lambdaSB_TV/(subsets*L_const), IterationsRegul, tol);  % (more memory efficent)
-                    end
-                else
-                    % 3D regularization
-                    X = SplitBregman_TV(single(X), lambdaSB_TV/(subsets*L_const), IterationsRegul, tol);  % (more memory efficent)
-                end
-            end
-            if (lambdaHO > 0)
-                % Higher Order (LLT) regularization
-                X2 = zeros(N,N,SlicesZ,'single');
-                if ((strcmp('2D', Dimension) == 1))
-                    % 2D regularization
-                    for kkk = 1:SlicesZ
-                        X2(:,:,kkk) = LLT_model(single(X(:,:,kkk)), lambdaHO/(subsets*L_const), tauHO/subsets, iterHO, 2.0e-05, 0);
-                    end
-                else
-                    % 3D regularization
-                    X2 = LLT_model(single(X), lambdaHO/(subsets*L_const), tauHO/subsets, iterHO, 2.0e-05, 0);
-                end
-                X = 0.5.*(X + X2); % the averaged combination of two solutions
-            end
-            if (lambdaPB > 0)
-                % Patch-Based regularization (can be slow on CPU)
-                if ((strcmp('2D', Dimension) == 1))
-                    % 2D regularization
-                    for kkk = 1:SlicesZ
-                        X(:,:,kkk) = PatchBased_Regul(single(X(:,:,kkk)), SearchW, SimilW, h_PB, lambdaPB/(subsets*L_const));
-                    end
-                else
-                    X = PatchBased_Regul(single(X), SearchW, SimilW, h_PB, lambdaPB/(subsets*L_const));
-                end
-            end
-            if (lambdaPB_GPU > 0)
-                % Patch-Based regularization (GPU CUDA implementation)
-                if ((strcmp('2D', Dimension) == 1))
-                    % 2D regularization
-                    for kkk = 1:SlicesZ
-                        X(:,:,kkk) = NLM_GPU(single(X(:,:,kkk)), SearchW, SimilW, h_PB, lambdaPB_GPU/(subsets*L_const));
-                    end
-                else
-                    X = NLM_GPU(single(X), SearchW, SimilW, h_PB, lambdaPB_GPU/(subsets*L_const));
-                end
-            end
-            if (LambdaDiff_HO > 0)
-                % Higher-order diffusion penalty (GPU CUDA implementation)
-                if ((strcmp('2D', Dimension) == 1))
-                    % 2D regularization
-                    for kkk = 1:SlicesZ
-                        X(:,:,kkk) = Diff4thHajiaboli_GPU(single(X(:,:,kkk)), LambdaDiff_HO_EdgePar, LambdaDiff_HO/(subsets*L_const), round(IterationsRegul/subsets));
-                    end
-                else
-                    X = Diff4thHajiaboli_GPU(X, LambdaDiff_HO_EdgePar, LambdaDiff_HO/(subsets*L_const), round(IterationsRegul/subsets));
-                end
-            end
-            if (LambdaTGV > 0)
-                % Total Generalized variation (currently only 2D)
-                lamTGV1 = 1.1; % smoothing trade-off parameters, see Pock's paper
-                lamTGV2 = 0.5; % second-order term
-                for kkk = 1:SlicesZ
-                    X(:,:,kkk) = TGV_PD(single(X(:,:,kkk)), LambdaTGV/(subsets*L_const), lamTGV1, lamTGV2, IterationsRegul);
-                end
-            end
-            
-            t = (1 + sqrt(1 + 4*t^2))/2; % updating t
-            X_t = X + ((t_old-1)/t).*(X - X_old); % updating X
-            counterInd = counterInd + numProjSub;
-        end
-        
-        if (i == 1)
-            r_old = r;
-        end
-        
-        % working with a 'ring vector'
-        if (lambdaR_L1 > 0)
-            r =  max(abs(r)-lambdaR_L1, 0).*sign(r); % soft-thresholding operator for ring vector
-            r_x = r + ((t_old-1)/t).*(r - r_old); % updating r
-        end
-        
-        if (show == 1)
-            figure(10); imshow(X(:,:,slice), [0 maxvalplot]);
-            if (lambdaR_L1 > 0)
-                figure(11); plot(r); title('Rings offset vector')
-            end
-            pause(0.01);
-        end
-        
-        if (strcmp(X_ideal, 'none' ) == 0)
-            Resid_error(i) = RMSE(X(ROI), X_ideal(ROI));
-            fprintf('%s %i %s %s %.4f  %s %s %f \n', 'Iteration Number:', i, '|', 'Error RMSE:', Resid_error(i), '|', 'Objective:', objective(i));
-        else
-            fprintf('%s %i  %s %s %f \n', 'Iteration Number:', i, '|', 'Objective:', objective(i));
-        end
-    end
-end
-
-output.Resid_error = Resid_error;
-output.objective = objective;
-output.L_const = L_const;
-
-end
diff --git a/main_func/regularizers_CPU/FGP_TV.c b/main_func/regularizers_CPU/FGP_TV.c
deleted file mode 100644
index 30cea1a..0000000
--- a/main_func/regularizers_CPU/FGP_TV.c
+++ /dev/null
@@ -1,216 +0,0 @@
-/*
-This work is part of the Core Imaging Library developed by
-Visual Analytics and Imaging System Group of the Science Technology
-Facilities Council, STFC
-
-Copyright 2017 Daniil Kazantsev
-Copyright 2017 Srikanth Nagella, Edoardo Pasca
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-*/
-#include "matrix.h"
-#include "mex.h"
-#include "FGP_TV_core.h"
-
-/* C-OMP implementation of FGP-TV [1] denoising/regularization model (2D/3D case)
- *
- * Input Parameters:
- * 1. Noisy image/volume [REQUIRED]
- * 2. lambda - regularization parameter [REQUIRED]
- * 3. Number of iterations [OPTIONAL parameter]
- * 4. eplsilon: tolerance constant [OPTIONAL parameter]
- * 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter]
- *
- * Output:
- * [1] Filtered/regularized image
- * [2] last function value 
- *
- * Example of image denoising:
- * figure;
- * Im = double(imread('lena_gray_256.tif'))/255;  % loading image
- * u0 = Im + .05*randn(size(Im)); % adding noise
- * u = FGP_TV(single(u0), 0.05, 100, 1e-04);
- *
- * to compile with OMP support: mex FGP_TV.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
- * This function is based on the Matlab's code and paper by
- * [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems"
- *
- * D. Kazantsev, 2016-17
- *
- */
-
-
-void mexFunction(
-        int nlhs, mxArray *plhs[],
-        int nrhs, const mxArray *prhs[])
-        
-{
-    int number_of_dims, iter, dimX, dimY, dimZ, ll, j, count, methTV;
-    const int  *dim_array;
-    float *A, *D=NULL, *D_old=NULL, *P1=NULL, *P2=NULL, *P3=NULL, *P1_old=NULL, *P2_old=NULL, *P3_old=NULL, *R1=NULL, *R2=NULL, *R3=NULL, lambda, tk, tkp1, re, re1, re_old, epsil;
-    
-    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
-    dim_array = mxGetDimensions(prhs[0]);
-    
-    /*Handling Matlab input data*/
-    if ((nrhs < 2) || (nrhs > 5)) mexErrMsgTxt("At least 2 parameters is required: Image(2D/3D), Regularization parameter. The full list of parameters: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1')");
-    
-    A  = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */
-    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
-    iter = 50; /* default iterations number */
-    epsil = 0.0001; /* default tolerance constant */
-    methTV = 0;  /* default isotropic TV penalty */
-    
-    if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5))  iter = (int) mxGetScalar(prhs[2]); /* iterations number */
-    if ((nrhs == 4) || (nrhs == 5))  epsil =  (float) mxGetScalar(prhs[3]); /* tolerance constant */
-    if (nrhs == 5)  {
-        char *penalty_type;
-        penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
-        if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
-        if (strcmp(penalty_type, "l1") == 0)  methTV = 1;  /* enable 'l1' penalty */
-        mxFree(penalty_type);
-    }
-    /*output function value (last iteration) */
-    plhs[1] = mxCreateNumericMatrix(1, 1, mxSINGLE_CLASS, mxREAL);  
-    float *funcvalA = (float *) mxGetData(plhs[1]);
-        
-    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
-    
-    /*Handling Matlab output data*/
-    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
-    
-    tk = 1.0f;
-    tkp1=1.0f;
-    count = 0;
-    re_old = 0.0f;
-    
-    if (number_of_dims == 2) {
-        dimZ = 1; /*2D case*/
-        D = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        D_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        P1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        P2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        P1_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        P2_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        R1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        R2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        
-        /* begin iterations */
-        for(ll=0; ll<iter; ll++) {
-            
-            /* computing the gradient of the objective function */
-            Obj_func2D(A, D, R1, R2, lambda, dimX, dimY);
-            
-            /*Taking a step towards minus of the gradient*/
-            Grad_func2D(P1, P2, D, R1, R2, lambda, dimX, dimY);
-            
-            /* projection step */
-            Proj_func2D(P1, P2, methTV, dimX, dimY);
-            
-            /*updating R and t*/
-            tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
-            Rupd_func2D(P1, P1_old, P2, P2_old, R1, R2, tkp1, tk, dimX, dimY);                
-            
-            /* calculate norm */
-            re = 0.0f; re1 = 0.0f;
-            for(j=0; j<dimX*dimY*dimZ; j++)
-            {
-                re += pow(D[j] - D_old[j],2);
-                re1 += pow(D[j],2);
-            }
-            re = sqrt(re)/sqrt(re1);
-            if (re < epsil)  count++;
-            if (count > 4) {
-                Obj_func_CALC2D(A, D, funcvalA, lambda, dimX, dimY); 
-                break; }
-            
-            /* check that the residual norm is decreasing */
-            if (ll > 2) {
-                if (re > re_old) {
-                    Obj_func_CALC2D(A, D, funcvalA, lambda, dimX, dimY);                                   
-                    break; }}            
-            re_old = re;
-            /*printf("%f %i %i \n", re, ll, count); */                      
-          
-            /*storing old values*/
-            copyIm(D, D_old, dimX, dimY, dimZ);
-            copyIm(P1, P1_old, dimX, dimY, dimZ);
-            copyIm(P2, P2_old, dimX, dimY, dimZ);
-            tk = tkp1;
-            
-            /* calculating the objective function value */
-            if (ll == (iter-1)) Obj_func_CALC2D(A, D, funcvalA, lambda, dimX, dimY);            
-        }
-        printf("FGP-TV iterations stopped at iteration %i with the function value %f \n", ll, funcvalA[0]);
-    }
-    if (number_of_dims == 3) {
-        D = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        D_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        P1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        P2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        P3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        P1_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        P2_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        P3_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        R1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        R2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        R3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        
-        /* begin iterations */
-        for(ll=0; ll<iter; ll++) {
-            
-            /* computing the gradient of the objective function */
-            Obj_func3D(A, D, R1, R2, R3,lambda, dimX, dimY, dimZ);
-            
-            /*Taking a step towards minus of the gradient*/
-            Grad_func3D(P1, P2, P3, D, R1, R2, R3, lambda, dimX, dimY, dimZ);
-            
-            /* projection step */
-            Proj_func3D(P1, P2, P3, dimX, dimY, dimZ);
-            
-            /*updating R and t*/
-            tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
-            Rupd_func3D(P1, P1_old, P2, P2_old, P3, P3_old, R1, R2, R3, tkp1, tk, dimX, dimY, dimZ);
-            
-            /* calculate norm - stopping rules*/
-            re = 0.0f; re1 = 0.0f;
-            for(j=0; j<dimX*dimY*dimZ; j++)
-            {
-                re += pow(D[j] - D_old[j],2);
-                re1 += pow(D[j],2);
-            }
-            re = sqrt(re)/sqrt(re1);
-            /* stop if the norm residual is less than the tolerance EPS */
-            if (re < epsil)  count++;
-            if (count > 3) {
-                Obj_func_CALC3D(A, D, funcvalA, lambda, dimX, dimY, dimZ);                            
-                break;}
-            
-            /* check that the residual norm is decreasing */
-            if (ll > 2) {
-                if (re > re_old) {
-                Obj_func_CALC3D(A, D, funcvalA, lambda, dimX, dimY, dimZ);
-                }}            
-            re_old = re;
-            /*printf("%f %i %i \n", re, ll, count); */
-            
-            /*storing old values*/
-            copyIm(D, D_old, dimX, dimY, dimZ);
-            copyIm(P1, P1_old, dimX, dimY, dimZ);
-            copyIm(P2, P2_old, dimX, dimY, dimZ);
-            copyIm(P3, P3_old, dimX, dimY, dimZ);
-            tk = tkp1;
-            
-            if (ll == (iter-1)) Obj_func_CALC3D(A, D, funcvalA, lambda, dimX, dimY, dimZ);            
-        }
-        printf("FGP-TV iterations stopped at iteration %i with the function value %f \n", ll, funcvalA[0]);
-    }
-}
diff --git a/main_func/regularizers_CPU/LLT_model.c b/main_func/regularizers_CPU/LLT_model.c
deleted file mode 100644
index 0b07b47..0000000
--- a/main_func/regularizers_CPU/LLT_model.c
+++ /dev/null
@@ -1,169 +0,0 @@
-/*
-This work is part of the Core Imaging Library developed by
-Visual Analytics and Imaging System Group of the Science Technology
-Facilities Council, STFC
-
-Copyright 2017 Daniil Kazantsev
-Copyright 2017 Srikanth Nagella, Edoardo Pasca
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-*/
-
-#include "mex.h"
-#include "matrix.h"
-#include "LLT_model_core.h"
-
-/* C-OMP implementation of Lysaker, Lundervold and Tai (LLT) model of higher order regularization penalty
-*
-* Input Parameters:
-* 1. U0 - original noise image/volume
-* 2. lambda - regularization parameter
-* 3. tau - time-step  for explicit scheme
-* 4. iter - iterations number
-* 5. epsil  - tolerance constant (to terminate earlier)
-* 6. switcher - default is 0, switch to (1) to restrictive smoothing in Z dimension (in test)
-*
-* Output:
-* Filtered/regularized image
-*
-* Example:
-* figure;
-* Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-* u0 = Im + .03*randn(size(Im)); % adding noise
-* [Den] = LLT_model(single(u0), 10, 0.1, 1);
-*
-*
-* to compile with OMP support: mex LLT_model.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
-* References: Lysaker, Lundervold and Tai (LLT) 2003, IEEE
-*
-* 28.11.16/Harwell
-*/
-
-void mexFunction(
-        int nlhs, mxArray *plhs[],
-        int nrhs, const mxArray *prhs[])
-        
-{
-    int number_of_dims, iter, dimX, dimY, dimZ, ll, j, count, switcher;
-    const int  *dim_array;
-    float *U0, *U=NULL, *U_old=NULL, *D1=NULL, *D2=NULL, *D3=NULL, lambda, tau, re, re1, epsil, re_old;
-    unsigned short *Map=NULL;
-    
-    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
-    dim_array = mxGetDimensions(prhs[0]);
-    
-    /*Handling Matlab input data*/
-    U0  = (float *) mxGetData(prhs[0]); /*origanal noise image/volume*/
-    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input in single precision is required"); }
-    lambda =  (float) mxGetScalar(prhs[1]); /*regularization parameter*/
-    tau =  (float) mxGetScalar(prhs[2]); /* time-step */
-    iter =  (int) mxGetScalar(prhs[3]); /*iterations number*/
-    epsil =  (float) mxGetScalar(prhs[4]); /* tolerance constant */
-    switcher =  (int) mxGetScalar(prhs[5]); /*switch on (1) restrictive smoothing in Z dimension*/
-     
-    /*Handling Matlab output data*/
-    dimX = dim_array[0]; dimY = dim_array[1];  dimZ = 1;
-    
-    if (number_of_dims == 2) {
-        /*2D case*/       
-        U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        D1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        D2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-    }
-    else if (number_of_dims == 3) {
-        /*3D case*/
-        dimZ = dim_array[2];
-        U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        U_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        D1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        D2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        D3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        if (switcher != 0) {
-        Map = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(3, dim_array, mxUINT16_CLASS, mxREAL));       
-        }
-    }
-    else {mexErrMsgTxt("The input data should be 2D or 3D");}
-    
-    /*Copy U0 to U*/
-    copyIm(U0, U, dimX, dimY, dimZ);
-    
-    count = 1;
-    re_old = 0.0f; 
-    if (number_of_dims == 2) {
-        for(ll = 0; ll < iter; ll++) {
-            
-            copyIm(U, U_old, dimX, dimY, dimZ);
-            
-            /*estimate inner derrivatives */
-            der2D(U, D1, D2, dimX, dimY, dimZ);
-            /* calculate div^2 and update */
-            div_upd2D(U0, U, D1, D2, dimX, dimY, dimZ, lambda, tau);
-            
-            /* calculate norm to terminate earlier */
-            re = 0.0f; re1 = 0.0f;
-            for(j=0; j<dimX*dimY*dimZ; j++)
-            {
-                re += pow(U_old[j] - U[j],2);
-                re1 += pow(U_old[j],2);
-            }
-            re = sqrt(re)/sqrt(re1);           
-            if (re < epsil)  count++;
-            if (count > 4) break;            
-            
-            /* check that the residual norm is decreasing */
-            if (ll > 2) {
-                if (re > re_old) break; 
-            }
-            re_old = re;         
-        
-        } /*end of iterations*/
-        printf("HO iterations stopped at iteration: %i\n", ll);          
-    }
-    /*3D version*/
-    if (number_of_dims == 3) {
-        
-        if (switcher == 1) {
-            /* apply restrictive smoothing */            
-            calcMap(U, Map, dimX, dimY, dimZ);
-            /*clear outliers */
-            cleanMap(Map, dimX, dimY, dimZ);           
-        }
-        for(ll = 0; ll < iter; ll++) {
-            
-            copyIm(U, U_old, dimX, dimY, dimZ);
-            
-            /*estimate inner derrivatives */
-            der3D(U, D1, D2, D3, dimX, dimY, dimZ);          
-            /* calculate div^2 and update */
-            div_upd3D(U0, U, D1, D2, D3, Map, switcher, dimX, dimY, dimZ, lambda, tau);                 
-            
-            /* calculate norm to terminate earlier */
-            re = 0.0f; re1 = 0.0f;
-            for(j=0; j<dimX*dimY*dimZ; j++)
-            {
-                re += pow(U_old[j] - U[j],2);
-                re1 += pow(U_old[j],2);
-            }
-            re = sqrt(re)/sqrt(re1);           
-            if (re < epsil)  count++;
-            if (count > 4) break;            
-            
-            /* check that the residual norm is decreasing */
-            if (ll > 2) {
-                if (re > re_old) break; 
-            }
-            re_old = re;     
-            
-        } /*end of iterations*/
-        printf("HO iterations stopped at iteration: %i\n", ll);       
-    }
-}
diff --git a/main_func/regularizers_CPU/SplitBregman_TV.c b/main_func/regularizers_CPU/SplitBregman_TV.c
deleted file mode 100644
index 38f6a9d..0000000
--- a/main_func/regularizers_CPU/SplitBregman_TV.c
+++ /dev/null
@@ -1,179 +0,0 @@
-/*
-This work is part of the Core Imaging Library developed by
-Visual Analytics and Imaging System Group of the Science Technology
-Facilities Council, STFC
-
-Copyright 2017 Daniil Kazantsev
-Copyright 2017 Srikanth Nagella, Edoardo Pasca
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-*/
-
-#include "mex.h"
-#include <matrix.h>
-#include "SplitBregman_TV_core.h"
-
-/* C-OMP implementation of Split Bregman - TV denoising-regularization model (2D/3D)
- *
- * Input Parameters:
- * 1. Noisy image/volume
- * 2. lambda - regularization parameter
- * 3. Number of iterations [OPTIONAL parameter]
- * 4. eplsilon - tolerance constant [OPTIONAL parameter]
- * 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter]
- *
- * Output:
- * Filtered/regularized image
- *
- * Example:
- * figure;
- * Im = double(imread('lena_gray_256.tif'))/255;  % loading image
- * u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
- * u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
- *
- * to compile with OMP support: mex SplitBregman_TV.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
- * References:
- * The Split Bregman Method for L1 Regularized Problems, by Tom Goldstein and Stanley Osher.
- * D. Kazantsev, 2016*
- */
-
-
-void mexFunction(
-        int nlhs, mxArray *plhs[],
-        int nrhs, const mxArray *prhs[])
-        
-{
-    int number_of_dims, iter, dimX, dimY, dimZ, ll, j, count, methTV;
-    const int  *dim_array;
-    float *A, *U=NULL, *U_old=NULL, *Dx=NULL, *Dy=NULL, *Dz=NULL, *Bx=NULL, *By=NULL, *Bz=NULL, lambda, mu, epsil, re, re1, re_old;
-    
-    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
-    dim_array = mxGetDimensions(prhs[0]);
-    
-    /*Handling Matlab input data*/
-    if ((nrhs < 2) || (nrhs > 5)) mexErrMsgTxt("At least 2 parameters is required: Image(2D/3D), Regularization parameter. The full list of parameters: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1')");
-    
-    /*Handling Matlab input data*/
-    A  = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */
-    mu =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
-    iter = 35; /* default iterations number */
-    epsil = 0.0001; /* default tolerance constant */
-    methTV = 0;  /* default isotropic TV penalty */
-    if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5))  iter = (int) mxGetScalar(prhs[2]); /* iterations number */
-    if ((nrhs == 4) || (nrhs == 5))  epsil =  (float) mxGetScalar(prhs[3]); /* tolerance constant */
-    if (nrhs == 5)  {
-        char *penalty_type;
-        penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
-        if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
-        if (strcmp(penalty_type, "l1") == 0)  methTV = 1;  /* enable 'l1' penalty */
-        mxFree(penalty_type);
-    }
-    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
-    
-    lambda = 2.0f*mu;
-    count = 1;
-    re_old = 0.0f;
-    /*Handling Matlab output data*/
-    dimY = dim_array[0]; dimX = dim_array[1]; dimZ = dim_array[2];
-    
-    if (number_of_dims == 2) {
-        dimZ = 1; /*2D case*/
-        U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        Dx = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        Dy = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        Bx = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        By = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        
-        copyIm(A, U, dimX, dimY, dimZ); /*initialize */
-        
-        /* begin outer SB iterations */
-        for(ll=0; ll<iter; ll++) {
-            
-            /*storing old values*/
-            copyIm(U, U_old, dimX, dimY, dimZ);
-            
-            /*GS iteration */
-            gauss_seidel2D(U, A, Dx, Dy, Bx, By, dimX, dimY, lambda, mu);
-            
-            if (methTV == 1)  updDxDy_shrinkAniso2D(U, Dx, Dy, Bx, By, dimX, dimY, lambda);
-            else updDxDy_shrinkIso2D(U, Dx, Dy, Bx, By, dimX, dimY, lambda);
-            
-            updBxBy2D(U, Dx, Dy, Bx, By, dimX, dimY);
-            
-            /* calculate norm to terminate earlier */
-            re = 0.0f; re1 = 0.0f;
-            for(j=0; j<dimX*dimY*dimZ; j++)
-            {
-                re += pow(U_old[j] - U[j],2);
-                re1 += pow(U_old[j],2);
-            }
-            re = sqrt(re)/sqrt(re1);
-            if (re < epsil)  count++;
-            if (count > 4) break;
-            
-            /* check that the residual norm is decreasing */
-            if (ll > 2) {
-                if (re > re_old) break;
-            }
-            re_old = re;
-            /*printf("%f %i %i \n", re, ll, count); */
-            
-            /*copyIm(U_old, U, dimX, dimY, dimZ); */
-        }
-        printf("SB iterations stopped at iteration: %i\n", ll);
-    }
-    if (number_of_dims == 3) {
-        U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        U_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        Dx = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        Dy = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        Dz = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        Bx = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        By = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        Bz = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
-        
-        copyIm(A, U, dimX, dimY, dimZ); /*initialize */
-        
-        /* begin outer SB iterations */
-        for(ll=0; ll<iter; ll++) {
-            
-            /*storing old values*/
-            copyIm(U, U_old, dimX, dimY, dimZ);
-            
-            /*GS iteration */
-            gauss_seidel3D(U, A, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda, mu);
-            
-            if (methTV == 1) updDxDyDz_shrinkAniso3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda);
-            else updDxDyDz_shrinkIso3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda);
-            
-            updBxByBz3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ);
-            
-            /* calculate norm to terminate earlier */
-            re = 0.0f; re1 = 0.0f;
-            for(j=0; j<dimX*dimY*dimZ; j++)
-            {
-                re += pow(U[j] - U_old[j],2);
-                re1 += pow(U[j],2);
-            }
-            re = sqrt(re)/sqrt(re1);
-            if (re < epsil)  count++;
-            if (count > 4) break;
-            
-            /* check that the residual norm is decreasing */
-            if (ll > 2) {
-                if (re > re_old) break; }
-            /*printf("%f %i %i \n", re, ll, count); */
-            re_old = re;
-        }
-        printf("SB iterations stopped at iteration: %i\n", ll);
-    }
-}
\ No newline at end of file
diff --git a/main_func/regularizers_CPU/TGV_PD.c b/main_func/regularizers_CPU/TGV_PD.c
deleted file mode 100644
index c9cb440..0000000
--- a/main_func/regularizers_CPU/TGV_PD.c
+++ /dev/null
@@ -1,144 +0,0 @@
-/*
-This work is part of the Core Imaging Library developed by
-Visual Analytics and Imaging System Group of the Science Technology
-Facilities Council, STFC
-
-Copyright 2017 Daniil Kazantsev
-Copyright 2017 Srikanth Nagella, Edoardo Pasca
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-*/
-
-#include "TGV_PD_core.h"
-#include "mex.h"
-
-/* C-OMP implementation of Primal-Dual denoising method for 
- * Total Generilized Variation (TGV)-L2 model (2D case only)
- *
- * Input Parameters:
- * 1. Noisy image/volume (2D)
- * 2. lambda - regularization parameter
- * 3. parameter to control first-order term (alpha1)
- * 4. parameter to control the second-order term (alpha0)
- * 5. Number of CP iterations
- *
- * Output:
- * Filtered/regularized image 
- *
- * Example:
- * figure;
- * Im = double(imread('lena_gray_256.tif'))/255;  % loading image
- * u0 = Im + .03*randn(size(Im)); % adding noise
- * tic; u = TGV_PD(single(u0), 0.02, 1.3, 1, 550); toc;
- *
- * to compile with OMP support: mex TGV_PD.c TGV_PD_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
- * References:
- * K. Bredies "Total Generalized Variation"
- *
- * 28.11.16/Harwell
- */
- 
-void mexFunction(
-        int nlhs, mxArray *plhs[],
-        int nrhs, const mxArray *prhs[])
-        
-{
-    int number_of_dims, iter, dimX, dimY, dimZ, ll;
-    const int  *dim_array;
-    float *A, *U, *U_old, *P1, *P2, *Q1, *Q2, *Q3, *V1, *V1_old, *V2, *V2_old, lambda, L2, tau, sigma,  alpha1, alpha0;
-    
-    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
-    dim_array = mxGetDimensions(prhs[0]);
-    
-    /*Handling Matlab input data*/
-    A  = (float *) mxGetData(prhs[0]); /*origanal noise image/volume*/
-    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input in single precision is required"); }
-    lambda =  (float) mxGetScalar(prhs[1]); /*regularization parameter*/
-    alpha1 =  (float) mxGetScalar(prhs[2]); /*first-order term*/
-    alpha0 =  (float) mxGetScalar(prhs[3]); /*second-order term*/
-    iter =  (int) mxGetScalar(prhs[4]); /*iterations number*/
-    if(nrhs != 5) mexErrMsgTxt("Five input parameters is reqired: Image(2D/3D), Regularization parameter, alpha1, alpha0, Iterations");
-    
-    /*Handling Matlab output data*/
-    dimX = dim_array[0]; dimY = dim_array[1];
-    
-    if (number_of_dims == 2) {
-        /*2D case*/
-        dimZ = 1;
-        U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        
-        /*dual variables*/
-        P1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        P2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        
-        Q1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        Q2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        Q3 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        
-        U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        
-        V1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        V1_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        V2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        V2_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));   
-        
-        
-         /*printf("%i \n", i);*/
-    L2 = 12.0f; /*Lipshitz constant*/
-    tau = 1.0/pow(L2,0.5);
-    sigma = 1.0/pow(L2,0.5);
-    
-    /*Copy A to U*/
-    copyIm(A, U, dimX, dimY, dimZ);
-    
-        /* Here primal-dual iterations begin for 2D */
-        for(ll = 0; ll < iter; ll++) {
-            
-            /* Calculate Dual Variable P */
-            DualP_2D(U, V1, V2, P1, P2, dimX, dimY, dimZ, sigma);
-            
-            /*Projection onto convex set for P*/
-            ProjP_2D(P1, P2, dimX, dimY, dimZ, alpha1);
-            
-            /* Calculate Dual Variable Q */
-            DualQ_2D(V1, V2, Q1, Q2, Q3, dimX, dimY, dimZ, sigma);
-            
-            /*Projection onto convex set for Q*/
-            ProjQ_2D(Q1, Q2, Q3, dimX, dimY, dimZ, alpha0);
-            
-            /*saving U into U_old*/
-            copyIm(U, U_old, dimX, dimY, dimZ);
-            
-            /*adjoint operation  -> divergence and projection of P*/
-            DivProjP_2D(U, A, P1, P2, dimX, dimY, dimZ, lambda, tau);
-            
-            /*get updated solution U*/
-            newU(U, U_old, dimX, dimY, dimZ);
-            
-            /*saving V into V_old*/
-            copyIm(V1, V1_old, dimX, dimY, dimZ);
-            copyIm(V2, V2_old, dimX, dimY, dimZ);
-            
-            /* upd V*/
-            UpdV_2D(V1, V2, P1, P2, Q1, Q2, Q3, dimX, dimY, dimZ, tau);
-            
-            /*get new V*/
-            newU(V1, V1_old, dimX, dimY, dimZ);
-            newU(V2, V2_old, dimX, dimY, dimZ);
-        } /*end of iterations*/        
-    }
-    else if (number_of_dims == 3) {
-        mexErrMsgTxt("The input data should be a 2D array");
-        /*3D case*/
-    }
-    else {mexErrMsgTxt("The input data should be a 2D array");}    
-   
-}
diff --git a/main_func/studentst.m b/main_func/studentst.m
deleted file mode 100644
index 93e0a0a..0000000
--- a/main_func/studentst.m
+++ /dev/null
@@ -1,47 +0,0 @@
-function [f,g,h,s,k] = studentst(r,k,s)

-% Students T penalty with 'auto-tuning'

-%

-% use:

-%   [f,g,h,{k,{s}}] = studentst(r)     - automatically fits s and k

-%   [f,g,h,{k,{s}}] = studentst(r,k)   - automatically fits s

-%   [f,g,h,{k,{s}}] = studentst(r,k,s) - use given s and k

-%

-% input:

-%   r - residual as column vector

-%   s - scale (optional)

-%   k - degrees of freedom (optional)

-% 

-% output:

-%   f - misfit (scalar)

-%   g - gradient (column vector)

-%   h - positive approximation of the Hessian (column vector, Hessian is a diagonal matrix)

-%   s,k - scale and degrees of freedom

-%

-% Tristan van Leeuwen, 2012.

-% tleeuwen@eos.ubc.ca

-

-% fit both s and k

-if nargin == 1

-    opts = optimset('maxFunEvals',1e2);

-    tmp = fminsearch(@(x)st(r,x(1),x(2)),[1;2],opts);

-    s   = tmp(1);

-    k   = tmp(2);

-end

-

-

-if nargin == 2

-    opts = optimset('maxFunEvals',1e2);

-    tmp = fminsearch(@(x)st(r,x,k),[1],opts);

-    s   = tmp(1);

-end

-

-% evaulate penalty

-[f,g,h] = st(r,s,k);

-

-

-function [f,g,h] = st(r,s,k)

-n = length(r);

-c = -n*(gammaln((k+1)/2) - gammaln(k/2) - .5*log(pi*s*k));

-f = c + .5*(k+1)*sum(log(1 + conj(r).*r/(s*k)));

-g = (k+1)*r./(s*k + conj(r).*r);

-h = (k+1)./(s*k + conj(r).*r);

diff --git a/src/Python/conda-recipe/meta.yaml b/src/Python/conda-recipe/meta.yaml
index 7068e9d..9ef9156 100644
--- a/src/Python/conda-recipe/meta.yaml
+++ b/src/Python/conda-recipe/meta.yaml
@@ -14,8 +14,8 @@ requirements:
     - python
     - numpy
     - setuptools
-    - boost ==1.64
-    - boost-cpp ==1.64
+    - boost ==1.65
+    - boost-cpp ==1.65
     - cython
 
   run: