diff options
Diffstat (limited to 'Wrappers/Matlab')
| -rw-r--r-- | Wrappers/Matlab/mex_compile/compile_mex.m | 3 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/regularizers_CPU/ROF_TV.c | 106 |
2 files changed, 108 insertions, 1 deletions
diff --git a/Wrappers/Matlab/mex_compile/compile_mex.m b/Wrappers/Matlab/mex_compile/compile_mex.m index e1debf3..ee85b49 100644 --- a/Wrappers/Matlab/mex_compile/compile_mex.m +++ b/Wrappers/Matlab/mex_compile/compile_mex.m @@ -7,13 +7,14 @@ cd regularizers_CPU/ % compile C regularizers +mex ROF_TV.c ROF_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" mex LLT_model.c LLT_model_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" mex FGP_TV.c FGP_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" mex SplitBregman_TV.c SplitBregman_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" mex TGV_PD.c TGV_PD_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" mex PatchBased_Regul.c PatchBased_Regul_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" -delete LLT_model_core.c LLT_model_core.h FGP_TV_core.c FGP_TV_core.h SplitBregman_TV_core.c SplitBregman_TV_core.h TGV_PD_core.c TGV_PD_core.h PatchBased_Regul_core.c PatchBased_Regul_core.h utils.c utils.h CCPiDefines.h +delete ROF_TV_core.c ROF_TV_core.h LLT_model_core.c LLT_model_core.h FGP_TV_core.c FGP_TV_core.h SplitBregman_TV_core.c SplitBregman_TV_core.h TGV_PD_core.c TGV_PD_core.h PatchBased_Regul_core.c PatchBased_Regul_core.h utils.c utils.h CCPiDefines.h % compile CUDA-based regularizers %cd regularizers_GPU/ diff --git a/Wrappers/Matlab/mex_compile/regularizers_CPU/ROF_TV.c b/Wrappers/Matlab/mex_compile/regularizers_CPU/ROF_TV.c new file mode 100644 index 0000000..a800add --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularizers_CPU/ROF_TV.c @@ -0,0 +1,106 @@ +/* + * 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 "ROF_TV_core.h" + +/* C-OMP implementation of ROF-TV denoising/regularization model [1] (2D/3D case) + * + * Input Parameters: + * 1. Noisy image/volume [REQUIRED] + * 2. lambda - regularization parameter [REQUIRED] + * 3. tau - marching step for explicit scheme, ~0.001 is recommended [REQUIRED] + * 4. Number of iterations, for explicit scheme >= 150 is recommended [REQUIRED] + * + * Output: + * [1] Regularized image/volume + * + * This function is based on the paper by + * [1] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms" + * compile: mex ROF_TV.c ROF_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" + * D. Kazantsev, 2016-18 + */ + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) + +{ + int i, number_of_dims, iter_numb, dimX, dimY, dimZ; + const int *dim_array; + float *A, *B, *D1, *D2, *D3, lambda, tau; + + dim_array = mxGetDimensions(prhs[0]); + number_of_dims = mxGetNumberOfDimensions(prhs[0]); + + /*Handling Matlab input data*/ + A = (float *) mxGetData(prhs[0]); + lambda = (float) mxGetScalar(prhs[1]); /* regularization parameter */ + tau = (float) mxGetScalar(prhs[2]); /* marching step parameter */ + iter_numb = (int) mxGetScalar(prhs[3]); /* iterations number */ + + 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]; + + /* output arrays*/ + if (number_of_dims == 2) { + dimZ = 0; /*2D case*/ + B = (float*)mxGetPr(plhs[0] = 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)); + + /* copy into B */ + copyIm(A, B, dimX, dimY, 1); + + /* start TV iterations */ + for(i=0; i < iter_numb; i++) { + + /* calculate differences */ + D1_func(B, D1, dimX, dimY, dimZ); + D2_func(B, D2, dimX, dimY, dimZ); + + /* calculate divergence and image update*/ + TV_main(D1, D2, D2, B, A, lambda, tau, dimX, dimY, dimZ); + } + } + + if (number_of_dims == 3) { + B = (float*)mxGetPr(plhs[0] = 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)); + + /* copy into B */ + copyIm(A, B, dimX, dimY, dimZ); + + /* start TV iterations */ + for(i=0; i < iter_numb; i++) { + + /* calculate differences */ + D1_func(B, D1, dimX, dimY, dimZ); + D2_func(B, D2, dimX, dimY, dimZ); + D3_func(B, D3, dimX, dimY, dimZ); + + /* calculate divergence and image update*/ + TV_main(D1, D2, D3, B, A, lambda, tau, dimX, dimY, dimZ); + } + } + +} |