Merge pull request #137 from vais-ral/pdtv

Adds primal-dual TV version for CPU/GPU

4 files changed, 213 insertions, 1 deletions

diff --git a/src/Matlab/mex_compile/compileCPU_mex_Linux.m b/src/Matlab/mex_compile/compileCPU_mex_Linux.m
index 2ed7ea6..5330d7f 100644
--- a/src/Matlab/mex_compile/compileCPU_mex_Linux.m
+++ b/src/Matlab/mex_compile/compileCPU_mex_Linux.m
@@ -28,6 +28,10 @@ movefile('FGP_TV.mex*',Pathmove);
 fprintf('%s \n', 'Compiling SB-TV...');
 mex SB_TV.c SB_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
 movefile('SB_TV.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling PD-TV...');
+mex PD_TV.c PD_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('PD_TV.mex*',Pathmove);
  
 fprintf('%s \n', 'Compiling dFGP-TV...');
 mex FGP_dTV.c FGP_dTV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
@@ -75,7 +79,8 @@ movefile('NonlocalMarching_Inpaint.mex*',Pathmove);
 delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* Diffusion_core* Diffus4th_order_core* TGV_core* LLT_ROF_core* CCPiDefines.h
 delete PatchSelect_core* Nonlocal_TV_core*
 delete Diffusion_Inpaint_core* NonlocalMarching_Inpaint_core*
-fprintf('%s \n', '<<<<<<< Regularisers successfully compiled! >>>>>>>');
+delete PD_TV_core*
+fprintf('%s \n', '<<<<<<< CPU regularisers were successfully compiled! >>>>>>>');
 
 pathA2 = sprintf(['..' fsep '..' fsep '..' fsep '..' fsep 'demos' fsep 'Matlab_demos'], 1i);
 cd(pathA2);
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/compileGPU_mex.m b/src/Matlab/mex_compile/compileGPU_mex.m
index 56fcd38..577630f 100644
--- a/src/Matlab/mex_compile/compileGPU_mex.m
+++ b/src/Matlab/mex_compile/compileGPU_mex.m
@@ -41,6 +41,11 @@ fprintf('%s \n', 'Compiling SB-TV...');
 mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu SB_TV_GPU.cpp TV_SB_GPU_core.o
 movefile('SB_TV_GPU.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling PD-TV...');
+!/usr/local/cuda/bin/nvcc -O0 -c TV_PD_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu PD_TV_GPU.cpp TV_PD_GPU_core.o
+movefile('PD_TV_GPU.mex*',Pathmove);
+
 fprintf('%s \n', 'Compiling TGV...');
 !/usr/local/cuda/bin/nvcc -O0 -c TGV_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
 mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu TGV_GPU.cpp TGV_GPU_core.o
@@ -72,6 +77,7 @@ mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcu
 movefile('PatchSelect_GPU.mex*',Pathmove);
 
 delete TV_ROF_GPU_core* TV_FGP_GPU_core* TV_SB_GPU_core* dTV_FGP_GPU_core* NonlDiff_GPU_core* Diffus_4thO_GPU_core* TGV_GPU_core* LLT_ROF_GPU_core* CCPiDefines.h
+delete TV_PD_GPU_core*
 delete PatchSelect_GPU_core* Nonlocal_TV_core* shared.h
 fprintf('%s \n', 'All successfully compiled!');
 
diff --git a/src/Matlab/mex_compile/regularisers_CPU/PD_TV.c b/src/Matlab/mex_compile/regularisers_CPU/PD_TV.c
new file mode 100644
index 0000000..e5ab1e4
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/PD_TV.c
@@ -0,0 +1,100 @@
+/*
+ * 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 2019 Daniil Kazantsev
+ * Copyright 2019 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 "PD_TV_core.h"
+
+/* C-OMP implementation of Primal-Dual TV [1] by Chambolle Pock denoising/regularization model (2D/3D case)
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume
+ * 2. lambdaPar - regularization parameter
+ * 3. Number of iterations
+ * 4. eplsilon: tolerance constant
+ * 5. TV-type: methodTV - 'iso' (0) or 'l1' (1)
+ * 6. nonneg: 'nonnegativity (0 is OFF by default, 1 is ON)
+ * 7. lipschitz_const: convergence related parameter
+ * 8. tau: convergence related parameter
+ 
+ * Output:
+ * [1] TV - Filtered/regularized image/volume
+ * [2] Information vector which contains [iteration no., reached tolerance]
+ *
+ * [1] Antonin Chambolle, Thomas Pock. "A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging", 2010
+ */
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter, methTV, nonneg;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    float *Input, *infovec=NULL, *Output=NULL, lambda, epsil, lipschitz_const, tau;
+    
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    dim_array = mxGetDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    if ((nrhs < 2) || (nrhs > 7)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1'), nonnegativity switch, lipschitz_const");
+    
+    Input  = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    iter = 500; /* default iterations number */
+    epsil = 1.0e-06; /* default tolerance constant */
+    methTV = 0;  /* default isotropic TV penalty */
+    nonneg = 0; /* default nonnegativity switch, off - 0 */
+    lipschitz_const = 8.0; /* lipschitz_const */
+    tau = 0.0025; /* tau convergence const */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    
+    if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8))  iter = (int) mxGetScalar(prhs[2]); /* iterations number */
+    if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8))  epsil =  (float) mxGetScalar(prhs[3]); /* tolerance constant */
+    if ((nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8))  {
+        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 ((nrhs == 6) || (nrhs == 7) || (nrhs == 8))  {
+        nonneg = (int) mxGetScalar(prhs[5]);
+        if ((nonneg != 0) && (nonneg != 1)) mexErrMsgTxt("Nonnegativity constraint can be enabled by choosing 1 or off - 0");
+    }
+    if ((nrhs == 7) || (nrhs == 8)) lipschitz_const = (float) mxGetScalar(prhs[6]);
+    if (nrhs == 8) tau = (float) mxGetScalar(prhs[7]);   
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];    
+       
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));        
+    }
+    if (number_of_dims == 3) {    
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    }    
+    mwSize vecdim[1];
+    vecdim[0] = 2;
+    infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));
+    
+    /* running the function */    
+    PDTV_CPU_main(Input, Output, infovec, lambda, iter,  epsil, lipschitz_const, methTV, nonneg, tau, dimX, dimY, dimZ);
+}
diff --git a/src/Matlab/mex_compile/regularisers_GPU/PD_TV_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/PD_TV_GPU.cpp
new file mode 100644
index 0000000..e853dd3
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_GPU/PD_TV_GPU.cpp
@@ -0,0 +1,101 @@
+/*
+ * 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 2019 Daniil Kazantsev
+ * Copyright 2019 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 "TV_PD_GPU_core.h"
+
+/* GPU implementation of Primal-Dual TV [1] by Chambolle Pock denoising/regularization model (2D/3D case)
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume
+ * 2. lambdaPar - regularization parameter
+ * 3. Number of iterations
+ * 4. eplsilon: tolerance constant
+ * 5. TV-type: methodTV - 'iso' (0) or 'l1' (1)
+ * 6. nonneg: 'nonnegativity (0 is OFF by default, 1 is ON)
+ * 7. lipschitz_const: convergence related parameter
+ * 8. tau: convergence related parameter
+ 
+ * Output:
+ * [1] TV - Filtered/regularized image/volume
+ * [2] Information vector which contains [iteration no., reached tolerance]
+ *
+ * [1] Antonin Chambolle, Thomas Pock. "A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging", 2010
+ */
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter, methTV, nonneg;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    float *Input, *infovec=NULL, *Output=NULL, lambda, epsil, lipschitz_const, tau;
+    
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    dim_array = mxGetDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    if ((nrhs < 2) || (nrhs > 7)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1'), nonnegativity switch, lipschitz_const");
+    
+    Input  = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    iter = 500; /* default iterations number */
+    epsil = 1.0e-06; /* default tolerance constant */
+    methTV = 0;  /* default isotropic TV penalty */
+    nonneg = 0; /* default nonnegativity switch, off - 0 */
+    lipschitz_const = 8.0; /* lipschitz_const */
+    tau = 0.0025; /* tau convergence const */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    
+    if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8))  iter = (int) mxGetScalar(prhs[2]); /* iterations number */
+    if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8))  epsil =  (float) mxGetScalar(prhs[3]); /* tolerance constant */
+    if ((nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8))  {
+        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 ((nrhs == 6) || (nrhs == 7) || (nrhs == 8))  {
+        nonneg = (int) mxGetScalar(prhs[5]);
+        if ((nonneg != 0) && (nonneg != 1)) mexErrMsgTxt("Nonnegativity constraint can be enabled by choosing 1 or off - 0");
+    }
+    if ((nrhs == 7) || (nrhs == 8)) lipschitz_const = (float) mxGetScalar(prhs[6]);
+    if (nrhs == 8) tau = (float) mxGetScalar(prhs[7]);   
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];    
+       
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));        
+    }
+    if (number_of_dims == 3) {    
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    }    
+    mwSize vecdim[1];
+    vecdim[0] = 2;
+    infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));
+    
+    /* running the function */    
+    TV_PD_GPU_main(Input, Output, infovec, lambda, iter,  epsil, lipschitz_const, methTV, nonneg, tau, dimX, dimY, dimZ);
+}