LLT-ROF model added

2 files changed, 92 insertions, 0 deletions

diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index cf81bec..bf9c861 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -21,6 +21,7 @@ cimport numpy as np
 cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
 cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
 cdef extern float SB_TV_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int printM, int dimX, int dimY, int dimZ);
+cdef extern float LLT_ROF_CPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
 cdef extern float TGV_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY);
 cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ);
 cdef extern float Diffus4th_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
@@ -222,7 +223,51 @@ def TGV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                        LipshitzConst,
                        dims[1],dims[0])                           
     return outputData
+
+#***************************************************************#
+#******************* ROF - LLT regularisation ******************#
+#***************************************************************#
+def LLT_ROF_CPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter):
+    if inputData.ndim == 2:
+        return LLT_ROF_2D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+    elif inputData.ndim == 3:
+        return LLT_ROF_3D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+
+def LLT_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameterROF,
+                     float regularisation_parameterLLT,
+                     int iterations, 
+                     float time_marching_parameter):
+                         
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+                   
+    #/* Run ROF-LLT iterations for 2D data */
+    LLT_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[1],dims[0],1)
+    return outputData
+
+def LLT_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameterROF,
+                     float regularisation_parameterLLT,
+                     int iterations, 
+                     float time_marching_parameter):
+						 
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
     
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0], dims[1], dims[2]], dtype='float32')
+           
+    #/* Run ROF-LLT iterations for 3D data */
+    LLT_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[2], dims[1], dims[0])
+    return outputData 
+
 #****************************************************************#
 #**************Directional Total-variation FGP ******************#
 #****************************************************************#
diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx
index 4a202d7..82d3e01 100644
--- a/Wrappers/Python/src/gpu_regularisers.pyx
+++ b/Wrappers/Python/src/gpu_regularisers.pyx
@@ -22,6 +22,7 @@ cdef extern void TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, i
 cdef extern void TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int N, int M, int Z);
 cdef extern void TV_SB_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int printM, int N, int M, int Z);
 cdef extern void TGV_GPU_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY);
+cdef extern void LLT_ROF_GPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int N, int M, int Z);
 cdef extern void NonlDiff_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int N, int M, int Z);
 cdef extern void dTV_FGP_GPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int N, int M, int Z);
 cdef extern void Diffus4th_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int N, int M, int Z);
@@ -87,6 +88,12 @@ def TV_SB_GPU(inputData,
                      tolerance_param,
                      methodTV,
                      printM)
+# LLT-ROF model
+def LLT_ROF_GPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter):
+    if inputData.ndim == 2:
+        return LLT_ROF_GPU2D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+    elif inputData.ndim == 3:
+        return LLT_ROF_GPU3D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
 # Total Generilised Variation (TGV)
 def TGV_GPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst):
     if inputData.ndim == 2:
@@ -324,6 +331,46 @@ def SBTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
     return outputData 
 
 #***************************************************************#
+#************************ LLT-ROF model ************************#
+#***************************************************************#
+#************Joint LLT-ROF model for higher order **************#
+def LLT_ROF_GPU2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameterROF,
+                     float regularisation_parameterLLT,
+                     int iterations, 
+                     float time_marching_parameter):
+    
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1]], dtype='float32')
+          
+    # Running CUDA code here    
+    LLT_ROF_GPU_main(&inputData[0,0], &outputData[0,0],regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[1],dims[0],1);
+    return outputData
+    
+def LLT_ROF_GPU3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameterROF,
+                     float regularisation_parameterLLT,
+                     int iterations, 
+                     float time_marching_parameter):
+    
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+          
+    # Running CUDA code here    
+    LLT_ROF_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[2], dims[1], dims[0]);
+    return outputData 
+
+
+#***************************************************************#
 #***************** Total Generalised Variation *****************#
 #***************************************************************#
 def TGV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,