TNV module added

3 files changed, 88 insertions, 1 deletions

diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m
index 129bedc..dab98dc 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -70,3 +70,14 @@ figure; imshow(u_fgp_dtv, [0 1]); title('FGP-dTV denoised image (CPU)');
 % tic; u_fgp_dtvG = FGP_dTV_GPU(single(u0), single(u_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; 
 % figure; imshow(u_fgp_dtvG, [0 1]); title('FGP-dTV denoised image (GPU)');
 %%
+fprintf('Denoise using the TNV prior (CPU) \n');
+slices = 5; N = 512;
+vol3D = zeros(N,N,slices, 'single');
+for i = 1:slices
+vol3D(:,:,i) = Im + .05*randn(size(Im)); 
+end
+vol3D(vol3D < 0) = 0;
+
+iter_tnv = 200; % number of TNV iterations
+tic; u_tnv = TNV(single(vol3D), lambda_reg, iter_tnv); toc; 
+figure; imshow(u_tnv(:,:,3), [0 1]); title('TNV denoised stack of channels (CPU)');
diff --git a/Wrappers/Matlab/mex_compile/compileCPU_mex.m b/Wrappers/Matlab/mex_compile/compileCPU_mex.m
index c3c82ff..9892d73 100644
--- a/Wrappers/Matlab/mex_compile/compileCPU_mex.m
+++ b/Wrappers/Matlab/mex_compile/compileCPU_mex.m
@@ -17,7 +17,10 @@ movefile SB_TV.mex* ../installed/
 mex FGP_dTV.c FGP_dTV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
 movefile FGP_dTV.mex* ../installed/
 
-delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* utils* CCPiDefines.h
+mex TNV.c TNV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile TNV.mex* ../installed/
+
+delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* CCPiDefines.h
 
 fprintf('%s \n', 'All successfully compiled!');
 
diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/TNV.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/TNV.c
new file mode 100644
index 0000000..e0584c4
--- /dev/null
+++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/TNV.c
@@ -0,0 +1,73 @@
+/*
+ * 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 "TNV_core.h"
+/*
+ * C-OMP implementation of Total Nuclear Variation regularisation model (2D + channels) [1]
+ * The code is modified from the implementation by Joan Duran <joan.duran@uib.es> see
+ * "denoisingPDHG_ipol.cpp" in Joans Collaborative Total Variation package
+ *
+ * Input Parameters:
+ * 1. Noisy volume of 2D + channel dimension, i.e. 3D volume
+ * 2. lambda - regularisation parameter
+ * 3. Number of iterations [OPTIONAL parameter]
+ * 4. eplsilon - tolerance constant [OPTIONAL parameter]
+ * 5. print information: 0 (off) or 1 (on)  [OPTIONAL parameter]
+ *
+ * Output:
+ * 1. Filtered/regularized image
+ *
+ * [1]. Duran, J., Moeller, M., Sbert, C. and Cremers, D., 2016. Collaborative total variation: a general framework for vectorial TV models. SIAM Journal on Imaging Sciences, 9(1), pp.116-151.
+ */
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter, dimX, dimY, dimZ;
+    const int  *dim_array;
+    float *Input, *Output=NULL, lambda, epsil;
+    
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    dim_array = mxGetDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    if ((nrhs < 2) || (nrhs > 4)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D + channels), Regularisation parameter, Regularization parameter, iterations number, tolerance");
+    
+    Input  = (float *) mxGetData(prhs[0]); /* noisy sequence of channels (2D + channels) */
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    iter = 1000; /* default iterations number */
+    epsil = 1.00e-05; /* default tolerance constant */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    
+    if ((nrhs == 3) || (nrhs == 4))  iter = (int) mxGetScalar(prhs[2]); /* iterations number */
+    if (nrhs == 4)  epsil =  (float) mxGetScalar(prhs[3]); /* tolerance constant */
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    if (number_of_dims == 2) mexErrMsgTxt("The input must be 3D: [X,Y,Channels]");
+    if (number_of_dims == 3) {
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+        /* running the function */
+        TNV_CPU_main(Input, Output, lambda, iter, epsil, dimX, dimY, dimZ);
+    }
+}
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