From d9e090a5e56de630f85f0bd4238991fb307ce46b Mon Sep 17 00:00:00 2001 From: Edoardo Pasca Date: Wed, 2 Aug 2017 15:21:05 +0100 Subject: initial split of C files to be used both by Matlab and Python the regularizers routines have been splitted to separate the Matlab from the C implementation. This will allow the concurrent use of the C code from Boost Python. --- main_func/regularizers_CPU/FGP_TV.c | 198 ++----------- main_func/regularizers_CPU/FGP_TV_core.c | 200 +++++++++++++ main_func/regularizers_CPU/FGP_TV_core.h | 63 ++++ main_func/regularizers_CPU/LLT_model.c | 324 ++------------------- main_func/regularizers_CPU/LLT_model_core.c | 300 +++++++++++++++++++ main_func/regularizers_CPU/LLT_model_core.h | 64 ++++ main_func/regularizers_CPU/PatchBased_Regul.c | 199 ++----------- main_func/regularizers_CPU/PatchBased_Regul_core.c | 220 ++++++++++++++ main_func/regularizers_CPU/PatchBased_Regul_core.h | 64 ++++ main_func/regularizers_CPU/SplitBregman_TV.c | 261 ++--------------- main_func/regularizers_CPU/SplitBregman_TV_core.c | 270 +++++++++++++++++ main_func/regularizers_CPU/SplitBregman_TV_core.h | 59 ++++ main_func/regularizers_CPU/TGV_PD.c | 207 ++----------- main_func/regularizers_CPU/TGV_PD_core.c | 217 ++++++++++++++ main_func/regularizers_CPU/TGV_PD_core.h | 64 ++++ 15 files changed, 1619 insertions(+), 1091 deletions(-) create mode 100644 main_func/regularizers_CPU/FGP_TV_core.c create mode 100644 main_func/regularizers_CPU/FGP_TV_core.h create mode 100644 main_func/regularizers_CPU/LLT_model_core.c create mode 100644 main_func/regularizers_CPU/LLT_model_core.h create mode 100644 main_func/regularizers_CPU/PatchBased_Regul_core.c create mode 100644 main_func/regularizers_CPU/PatchBased_Regul_core.h create mode 100644 main_func/regularizers_CPU/SplitBregman_TV_core.c create mode 100644 main_func/regularizers_CPU/SplitBregman_TV_core.h create mode 100644 main_func/regularizers_CPU/TGV_PD_core.c create mode 100644 main_func/regularizers_CPU/TGV_PD_core.h (limited to 'main_func') diff --git a/main_func/regularizers_CPU/FGP_TV.c b/main_func/regularizers_CPU/FGP_TV.c index 1a1fd13..5d8cfb9 100644 --- a/main_func/regularizers_CPU/FGP_TV.c +++ b/main_func/regularizers_CPU/FGP_TV.c @@ -1,10 +1,23 @@ +/* +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 Kazanteev +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 -#include -#include -#include -#include -#include "omp.h" +#include "FGP_TV_core.h" /* C-OMP implementation of FGP-TV [1] denoising/regularization model (2D/3D case) * @@ -33,17 +46,6 @@ * */ -float copyIm(float *A, float *B, int dimX, int dimY, int dimZ); -float Obj_func2D(float *A, float *D, float *R1, float *R2, float lambda, int dimX, int dimY); -float Grad_func2D(float *P1, float *P2, float *D, float *R1, float *R2, float lambda, int dimX, int dimY); -float Proj_func2D(float *P1, float *P2, int methTV, int dimX, int dimY); -float Rupd_func2D(float *P1, float *P1_old, float *P2, float *P2_old, float *R1, float *R2, float tkp1, float tk, int dimX, int dimY); - -float Obj_func3D(float *A, float *D, float *R1, float *R2, float *R3, float lambda, int dimX, int dimY, int dimZ); -float Grad_func3D(float *P1, float *P2, float *P3, float *D, float *R1, float *R2, float *R3, float lambda, int dimX, int dimY, int dimZ); -float Proj_func3D(float *P1, float *P2, float *P3, int dimX, int dimY, int dimZ); -float Rupd_func3D(float *P1, float *P1_old, float *P2, float *P2_old, float *P3, float *P3_old, float *R1, float *R2, float *R3, float tkp1, float tk, int dimX, int dimY, int dimZ); - void mexFunction( int nlhs, mxArray *plhs[], @@ -236,165 +238,3 @@ void mexFunction( printf("FGP-TV iterations stopped at iteration %i with the function value %f \n", ll, funcvalA[0]); } } - -/* 2D-case related Functions */ -/*****************************************************************/ -float Obj_func2D(float *A, float *D, float *R1, float *R2, float lambda, int dimX, int dimY) -{ - float val1, val2; - int i,j; -#pragma omp parallel for shared(A,D,R1,R2) private(i,j,val1,val2) - for(i=0; i 1) { - P1[(i)*dimY + (j)] = P1[(i)*dimY + (j)]/sqrt(denom); - P2[(i)*dimY + (j)] = P2[(i)*dimY + (j)]/sqrt(denom); - } - }} - } - else { - /* anisotropic TV*/ -#pragma omp parallel for shared(P1,P2) private(i,j,val1,val2) - for(i=0; i 1) { + P1[(i)*dimY + (j)] = P1[(i)*dimY + (j)] / sqrt(denom); + P2[(i)*dimY + (j)] = P2[(i)*dimY + (j)] / sqrt(denom); + } + } + } + } + else { + /* anisotropic TV*/ +#pragma omp parallel for shared(P1,P2) private(i,j,val1,val2) + for (i = 0; i +#include +#include +#include +#include +#include "omp.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 +* +*/ + +float copyIm(float *A, float *B, int dimX, int dimY, int dimZ); +float Obj_func2D(float *A, float *D, float *R1, float *R2, float lambda, int dimX, int dimY); +float Grad_func2D(float *P1, float *P2, float *D, float *R1, float *R2, float lambda, int dimX, int dimY); +float Proj_func2D(float *P1, float *P2, int methTV, int dimX, int dimY); +float Rupd_func2D(float *P1, float *P1_old, float *P2, float *P2_old, float *R1, float *R2, float tkp1, float tk, int dimX, int dimY); + +float Obj_func3D(float *A, float *D, float *R1, float *R2, float *R3, float lambda, int dimX, int dimY, int dimZ); +float Grad_func3D(float *P1, float *P2, float *P3, float *D, float *R1, float *R2, float *R3, float lambda, int dimX, int dimY, int dimZ); +float Proj_func3D(float *P1, float *P2, float *P3, int dimX, int dimY, int dimZ); +float Rupd_func3D(float *P1, float *P1_old, float *P2, float *P2_old, float *P3, float *P3_old, float *R1, float *R2, float *R3, float tkp1, float tk, int dimX, int dimY, int dimZ); diff --git a/main_func/regularizers_CPU/LLT_model.c b/main_func/regularizers_CPU/LLT_model.c index 0aed31e..6b50d33 100644 --- a/main_func/regularizers_CPU/LLT_model.c +++ b/main_func/regularizers_CPU/LLT_model.c @@ -1,49 +1,25 @@ -#include "mex.h" -#include -#include -#include -#include -#include -#include "omp.h" - -#define EPS 0.01 +/* +This work is part of the Core Imaging Library developed by +Visual Analytics and Imaging System Group of the Science Technology +Facilities Council, STFC -/* C-OMP implementation of Lysaker, Lundervold and Tai (LLT) model of higher order regularization penalty - * - * Input Parameters: - * 1. U0 - origanal 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 - */ -/* 2D functions */ -float der2D(float *U, float *D1, float *D2, int dimX, int dimY, int dimZ); -float div_upd2D(float *U0, float *U, float *D1, float *D2, int dimX, int dimY, int dimZ, float lambda, float tau); +Copyright 2017 Daniil Kazanteev +Copyright 2017 Srikanth Nagella, Edoardo Pasca -float der3D(float *U, float *D1, float *D2, float *D3, int dimX, int dimY, int dimZ); -float div_upd3D(float *U0, float *U, float *D1, float *D2, float *D3, unsigned short *Map, int switcher, int dimX, int dimY, int dimZ, float lambda, float tau); +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. +*/ -float calcMap(float *U, unsigned short *Map, int dimX, int dimY, int dimZ); -float cleanMap(unsigned short *Map, int dimX, int dimY, int dimZ); +#include "mex.h" +#include "LLT_model_core.h" -float copyIm(float *A, float *U, int dimX, int dimY, int dimZ); void mexFunction( int nlhs, mxArray *plhs[], @@ -165,267 +141,3 @@ void mexFunction( printf("HO iterations stopped at iteration: %i\n", ll); } } - -float der2D(float *U, float *D1, float *D2, int dimX, int dimY, int dimZ) -{ - int i, j, i_p, i_m, j_m, j_p; - float dxx, dyy, denom_xx, denom_yy; -#pragma omp parallel for shared(U,D1,D2) private(i, j, i_p, i_m, j_m, j_p, denom_xx, denom_yy, dxx, dyy) - for(i=0; i= dimZ) k_p1 = k - 2; -// k_m1 = k - 2; if (k_m1 < 0) k_m1 = k + 2; - - dxx = D1[dimX*dimY*k + i_p*dimY + j] - 2.0f*D1[dimX*dimY*k + i*dimY + j] + D1[dimX*dimY*k + i_m*dimY + j]; - dyy = D2[dimX*dimY*k + i*dimY + j_p] - 2.0f*D2[dimX*dimY*k + i*dimY + j] + D2[dimX*dimY*k + i*dimY + j_m]; - dzz = D3[dimX*dimY*k_p + i*dimY + j] - 2.0f*D3[dimX*dimY*k + i*dimY + j] + D3[dimX*dimY*k_m + i*dimY + j]; - - if ((switcher == 1) && (Map[dimX*dimY*k + i*dimY + j] == 0)) dzz = 0; - div = dxx + dyy + dzz; - -// if (switcher == 1) { - // if (Map2[dimX*dimY*k + i*dimY + j] == 0) dzz2 = 0; - //else dzz2 = D4[dimX*dimY*k_p1 + i*dimY + j] - 2.0f*D4[dimX*dimY*k + i*dimY + j] + D4[dimX*dimY*k_m1 + i*dimY + j]; -// div = dzz + dzz2; -// } - -// dzz = D3[dimX*dimY*k_p + i*dimY + j] - 2.0f*D3[dimX*dimY*k + i*dimY + j] + D3[dimX*dimY*k_m + i*dimY + j]; -// dzz2 = D4[dimX*dimY*k_p1 + i*dimY + j] - 2.0f*D4[dimX*dimY*k + i*dimY + j] + D4[dimX*dimY*k_m1 + i*dimY + j]; -// div = dzz + dzz2; - - U[dimX*dimY*k + i*dimY + j] = U[dimX*dimY*k + i*dimY + j] - tau*div - tau*lambda*(U[dimX*dimY*k + i*dimY + j] - U0[dimX*dimY*k + i*dimY + j]); - }}} - return *U0; - } - -// float der3D_2(float *U, float *D1, float *D2, float *D3, float *D4, int dimX, int dimY, int dimZ) -// { -// int i, j, k, i_p, i_m, j_m, j_p, k_p, k_m, k_p1, k_m1; -// float dxx, dyy, dzz, dzz2, denom_xx, denom_yy, denom_zz, denom_zz2; -// #pragma omp parallel for shared(U,D1,D2,D3,D4) private(i, j, k, i_p, i_m, j_m, j_p, k_p, k_m, denom_xx, denom_yy, denom_zz, denom_zz2, dxx, dyy, dzz, dzz2, k_p1, k_m1) -// for(i=0; i= dimZ) k_p1 = k - 2; -// k_m1 = k - 2; if (k_m1 < 0) k_m1 = k + 2; -// -// dxx = U[dimX*dimY*k + i_p*dimY + j] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k + i_m*dimY + j]; -// dyy = U[dimX*dimY*k + i*dimY + j_p] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k + i*dimY + j_m]; -// dzz = U[dimX*dimY*k_p + i*dimY + j] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k_m + i*dimY + j]; -// dzz2 = U[dimX*dimY*k_p1 + i*dimY + j] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k_m1 + i*dimY + j]; -// -// denom_xx = fabs(dxx) + EPS; -// denom_yy = fabs(dyy) + EPS; -// denom_zz = fabs(dzz) + EPS; -// denom_zz2 = fabs(dzz2) + EPS; -// -// D1[dimX*dimY*k + i*dimY + j] = dxx/denom_xx; -// D2[dimX*dimY*k + i*dimY + j] = dyy/denom_yy; -// D3[dimX*dimY*k + i*dimY + j] = dzz/denom_zz; -// D4[dimX*dimY*k + i*dimY + j] = dzz2/denom_zz2; -// }}} -// return 1; -// } - -float calcMap(float *U, unsigned short *Map, int dimX, int dimY, int dimZ) -{ - int i,j,k,i1,j1,i2,j2,windowSize; - float val1, val2,thresh_val,maxval; - windowSize = 1; - thresh_val = 0.0001; /*thresh_val = 0.0035;*/ - - /* normalize volume first */ - maxval = 0.0f; - for(i=0; i maxval) maxval = U[dimX*dimY*k + i*dimY + j]; - }}} - - if (maxval != 0.0f) { - for(i=0; i= 0) && (i2 < dimX) && (j2 >= 0) && (j2 < dimY)) { - if (k == 0) { - val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+1) + i2*dimY + j2],2); -// val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+2) + i2*dimY + j2],2); - } - else if (k == dimZ-1) { - val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-1) + i2*dimY + j2],2); -// val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-2) + i2*dimY + j2],2); - } -// else if (k == 1) { -// val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-1) + i2*dimY + j2],2); -// val2 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+1) + i2*dimY + j2],2); -// val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+2) + i2*dimY + j2],2); -// } -// else if (k == dimZ-2) { -// val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-1) + i2*dimY + j2],2); -// val2 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+1) + i2*dimY + j2],2); -// val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-2) + i2*dimY + j2],2); -// } - else { - val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-1) + i2*dimY + j2],2); - val2 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+1) + i2*dimY + j2],2); -// val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-2) + i2*dimY + j2],2); -// val4 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+2) + i2*dimY + j2],2); - } - } - }} - - val1 = 0.111f*val1; val2 = 0.111f*val2; -// val3 = 0.111f*val3; val4 = 0.111f*val4; - if ((val1 <= thresh_val) && (val2 <= thresh_val)) Map[dimX*dimY*k + i*dimY + j] = 1; -// if ((val3 <= thresh_val) && (val4 <= thresh_val)) Map2[dimX*dimY*k + i*dimY + j] = 1; - }}} - return 1; -} - -float cleanMap(unsigned short *Map, int dimX, int dimY, int dimZ) -{ - int i, j, k, i1, j1, i2, j2, counter; - #pragma omp parallel for shared(Map) private(i, j, k, i1, j1, i2, j2, counter) - for(i=0; i= 0) && (i2 < dimX) && (j2 >= 0) && (j2 < dimY)) { - if (Map[dimX*dimY*k + i2*dimY + j2] == 0) counter++; - } - }} - if (counter < 24) Map[dimX*dimY*k + i*dimY + j] = 1; - }}} - return *Map; -} - - /* Copy Image */ - float copyIm(float *A, float *U, int dimX, int dimY, int dimZ) - { - int j; -#pragma omp parallel for shared(A, U) private(j) - for(j=0; j= dimZ) k_p1 = k - 2; + // k_m1 = k - 2; if (k_m1 < 0) k_m1 = k + 2; + + dxx = D1[dimX*dimY*k + i_p*dimY + j] - 2.0f*D1[dimX*dimY*k + i*dimY + j] + D1[dimX*dimY*k + i_m*dimY + j]; + dyy = D2[dimX*dimY*k + i*dimY + j_p] - 2.0f*D2[dimX*dimY*k + i*dimY + j] + D2[dimX*dimY*k + i*dimY + j_m]; + dzz = D3[dimX*dimY*k_p + i*dimY + j] - 2.0f*D3[dimX*dimY*k + i*dimY + j] + D3[dimX*dimY*k_m + i*dimY + j]; + + if ((switcher == 1) && (Map[dimX*dimY*k + i*dimY + j] == 0)) dzz = 0; + div = dxx + dyy + dzz; + + // if (switcher == 1) { + // if (Map2[dimX*dimY*k + i*dimY + j] == 0) dzz2 = 0; + //else dzz2 = D4[dimX*dimY*k_p1 + i*dimY + j] - 2.0f*D4[dimX*dimY*k + i*dimY + j] + D4[dimX*dimY*k_m1 + i*dimY + j]; + // div = dzz + dzz2; + // } + + // dzz = D3[dimX*dimY*k_p + i*dimY + j] - 2.0f*D3[dimX*dimY*k + i*dimY + j] + D3[dimX*dimY*k_m + i*dimY + j]; + // dzz2 = D4[dimX*dimY*k_p1 + i*dimY + j] - 2.0f*D4[dimX*dimY*k + i*dimY + j] + D4[dimX*dimY*k_m1 + i*dimY + j]; + // div = dzz + dzz2; + + U[dimX*dimY*k + i*dimY + j] = U[dimX*dimY*k + i*dimY + j] - tau*div - tau*lambda*(U[dimX*dimY*k + i*dimY + j] - U0[dimX*dimY*k + i*dimY + j]); + } + } + } + return *U0; +} + +// float der3D_2(float *U, float *D1, float *D2, float *D3, float *D4, int dimX, int dimY, int dimZ) +// { +// int i, j, k, i_p, i_m, j_m, j_p, k_p, k_m, k_p1, k_m1; +// float dxx, dyy, dzz, dzz2, denom_xx, denom_yy, denom_zz, denom_zz2; +// #pragma omp parallel for shared(U,D1,D2,D3,D4) private(i, j, k, i_p, i_m, j_m, j_p, k_p, k_m, denom_xx, denom_yy, denom_zz, denom_zz2, dxx, dyy, dzz, dzz2, k_p1, k_m1) +// for(i=0; i= dimZ) k_p1 = k - 2; +// k_m1 = k - 2; if (k_m1 < 0) k_m1 = k + 2; +// +// dxx = U[dimX*dimY*k + i_p*dimY + j] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k + i_m*dimY + j]; +// dyy = U[dimX*dimY*k + i*dimY + j_p] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k + i*dimY + j_m]; +// dzz = U[dimX*dimY*k_p + i*dimY + j] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k_m + i*dimY + j]; +// dzz2 = U[dimX*dimY*k_p1 + i*dimY + j] - 2.0f*U[dimX*dimY*k + i*dimY + j] + U[dimX*dimY*k_m1 + i*dimY + j]; +// +// denom_xx = fabs(dxx) + EPS; +// denom_yy = fabs(dyy) + EPS; +// denom_zz = fabs(dzz) + EPS; +// denom_zz2 = fabs(dzz2) + EPS; +// +// D1[dimX*dimY*k + i*dimY + j] = dxx/denom_xx; +// D2[dimX*dimY*k + i*dimY + j] = dyy/denom_yy; +// D3[dimX*dimY*k + i*dimY + j] = dzz/denom_zz; +// D4[dimX*dimY*k + i*dimY + j] = dzz2/denom_zz2; +// }}} +// return 1; +// } + +float calcMap(float *U, unsigned short *Map, int dimX, int dimY, int dimZ) +{ + int i, j, k, i1, j1, i2, j2, windowSize; + float val1, val2, thresh_val, maxval; + windowSize = 1; + thresh_val = 0.0001; /*thresh_val = 0.0035;*/ + + /* normalize volume first */ + maxval = 0.0f; + for (i = 0; i maxval) maxval = U[dimX*dimY*k + i*dimY + j]; + } + } + } + + if (maxval != 0.0f) { + for (i = 0; i= 0) && (i2 < dimX) && (j2 >= 0) && (j2 < dimY)) { + if (k == 0) { + val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k + 1) + i2*dimY + j2], 2); + // val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+2) + i2*dimY + j2],2); + } + else if (k == dimZ - 1) { + val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k - 1) + i2*dimY + j2], 2); + // val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-2) + i2*dimY + j2],2); + } + // else if (k == 1) { + // val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-1) + i2*dimY + j2],2); + // val2 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+1) + i2*dimY + j2],2); + // val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+2) + i2*dimY + j2],2); + // } + // else if (k == dimZ-2) { + // val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-1) + i2*dimY + j2],2); + // val2 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+1) + i2*dimY + j2],2); + // val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-2) + i2*dimY + j2],2); + // } + else { + val1 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k - 1) + i2*dimY + j2], 2); + val2 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k + 1) + i2*dimY + j2], 2); + // val3 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k-2) + i2*dimY + j2],2); + // val4 += pow(U[dimX*dimY*k + i2*dimY + j2] - U[dimX*dimY*(k+2) + i2*dimY + j2],2); + } + } + } + } + + val1 = 0.111f*val1; val2 = 0.111f*val2; + // val3 = 0.111f*val3; val4 = 0.111f*val4; + if ((val1 <= thresh_val) && (val2 <= thresh_val)) Map[dimX*dimY*k + i*dimY + j] = 1; + // if ((val3 <= thresh_val) && (val4 <= thresh_val)) Map2[dimX*dimY*k + i*dimY + j] = 1; + } + } + } + return 1; +} + +float cleanMap(unsigned short *Map, int dimX, int dimY, int dimZ) +{ + int i, j, k, i1, j1, i2, j2, counter; +#pragma omp parallel for shared(Map) private(i, j, k, i1, j1, i2, j2, counter) + for (i = 0; i= 0) && (i2 < dimX) && (j2 >= 0) && (j2 < dimY)) { + if (Map[dimX*dimY*k + i2*dimY + j2] == 0) counter++; + } + } + } + if (counter < 24) Map[dimX*dimY*k + i*dimY + j] = 1; + } + } + } + return *Map; +} + +/* Copy Image */ +float copyIm(float *A, float *U, int dimX, int dimY, int dimZ) +{ + int j; +#pragma omp parallel for shared(A, U) private(j) + for (j = 0; j +#include +#include +#include +#include +#include "omp.h" + +#define EPS 0.01 + +/* C-OMP implementation of Lysaker, Lundervold and Tai (LLT) model of higher order regularization penalty +* +* Input Parameters: +* 1. U0 - origanal 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 +*/ +/* 2D functions */ +float der2D(float *U, float *D1, float *D2, int dimX, int dimY, int dimZ); +float div_upd2D(float *U0, float *U, float *D1, float *D2, int dimX, int dimY, int dimZ, float lambda, float tau); + +float der3D(float *U, float *D1, float *D2, float *D3, int dimX, int dimY, int dimZ); +float div_upd3D(float *U0, float *U, float *D1, float *D2, float *D3, unsigned short *Map, int switcher, int dimX, int dimY, int dimZ, float lambda, float tau); + +float calcMap(float *U, unsigned short *Map, int dimX, int dimY, int dimZ); +float cleanMap(unsigned short *Map, int dimX, int dimY, int dimZ); + +float copyIm(float *A, float *U, int dimX, int dimY, int dimZ); diff --git a/main_func/regularizers_CPU/PatchBased_Regul.c b/main_func/regularizers_CPU/PatchBased_Regul.c index 1ed29d4..24ee210 100644 --- a/main_func/regularizers_CPU/PatchBased_Regul.c +++ b/main_func/regularizers_CPU/PatchBased_Regul.c @@ -1,12 +1,25 @@ -#define _USE_MATH_DEFINES +/* +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 Kazanteev +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 -#include -#include -#include -#include -#include "omp.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 @@ -41,9 +54,6 @@ * Harwell, 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); -float PB_FUNC2D(float *A, float *B, int dimX, int dimY, int padXY, int SearchW, int SimilW, float h, float lambda); -float PB_FUNC3D(float *A, float *B, int dimX, int dimY, int dimZ, int padXY, int SearchW, int SimilW, float h, float lambda); void mexFunction( int nlhs, mxArray *plhs[], @@ -125,171 +135,4 @@ void mexFunction( switchpad_crop = 1; /*cropping*/ pad_crop(Bp, B, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop); } /*end else ndims*/ -} - -/*2D version*/ -float PB_FUNC2D(float *A, float *B, int dimX, int dimY, int padXY, int SearchW, int SimilW, float h, float lambda) -{ - int i, j, i_n, j_n, i_m, j_m, i_p, j_p, i_l, j_l, i1, j1, i2, j2, i3, j3, i5,j5, count, SimilW_full; - float *Eucl_Vec, h2, denh2, normsum, Weight, Weight_norm, value, denom, WeightGlob, t1; - - /*SearchW_full = 2*SearchW + 1; */ /* the full searching window size */ - SimilW_full = 2*SimilW + 1; /* the full similarity window size */ - h2 = h*h; - denh2 = 1/(2*h2); - - /*Gaussian kernel */ - Eucl_Vec = (float*) calloc (SimilW_full*SimilW_full,sizeof(float)); - count = 0; - for(i_n=-SimilW; i_n<=SimilW; i_n++) { - for(j_n=-SimilW; j_n<=SimilW; j_n++) { - t1 = pow(((float)i_n), 2) + pow(((float)j_n), 2); - Eucl_Vec[count] = exp(-(t1)/(2*SimilW*SimilW)); - count = count + 1; - }} /*main neighb loop */ - - /*The NLM code starts here*/ - /* setting OMP here */ - #pragma omp parallel for shared (A, B, dimX, dimY, Eucl_Vec, lambda, denh2) private(denom, i, j, WeightGlob, count, i1, j1, i2, j2, i3, j3, i5, j5, Weight_norm, normsum, i_m, j_m, i_n, j_n, i_l, j_l, i_p, j_p, Weight, value) - - for(i=0; i= padXY) && (i < dimX-padXY)) && ((j >= padXY) && (j < dimY-padXY))) { - - /* Massive Search window loop */ - Weight_norm = 0; value = 0.0; - for(i_m=-SearchW; i_m<=SearchW; i_m++) { - for(j_m=-SearchW; j_m<=SearchW; j_m++) { - /*checking boundaries*/ - i1 = i+i_m; j1 = j+j_m; - - WeightGlob = 0.0; - /* if inside the searching window */ - for(i_l=-SimilW; i_l<=SimilW; i_l++) { - for(j_l=-SimilW; j_l<=SimilW; j_l++) { - i2 = i1+i_l; j2 = j1+j_l; - - i3 = i+i_l; j3 = j+j_l; /*coordinates of the inner patch loop */ - - count = 0; normsum = 0.0; - for(i_p=-SimilW; i_p<=SimilW; i_p++) { - for(j_p=-SimilW; j_p<=SimilW; j_p++) { - i5 = i2 + i_p; j5 = j2 + j_p; - normsum = normsum + Eucl_Vec[count]*pow(A[(i3+i_p)*dimY+(j3+j_p)]-A[i5*dimY+j5], 2); - count = count + 1; - }} - if (normsum != 0) Weight = (exp(-normsum*denh2)); - else Weight = 0.0; - WeightGlob += Weight; - }} - - value += A[i1*dimY+j1]*WeightGlob; - Weight_norm += WeightGlob; - }} /*search window loop end*/ - - /* the final loop to average all values in searching window with weights */ - denom = 1 + lambda*Weight_norm; - B[i*dimY+j] = (A[i*dimY+j] + lambda*value)/denom; - } - }} /*main loop*/ - return (*B); - free(Eucl_Vec); -} - -/*3D version*/ - float PB_FUNC3D(float *A, float *B, int dimX, int dimY, int dimZ, int padXY, int SearchW, int SimilW, float h, float lambda) - { - int SimilW_full, count, i, j, k, i_n, j_n, k_n, i_m, j_m, k_m, i_p, j_p, k_p, i_l, j_l, k_l, i1, j1, k1, i2, j2, k2, i3, j3, k3, i5, j5, k5; - float *Eucl_Vec, h2, denh2, normsum, Weight, Weight_norm, value, denom, WeightGlob; - - /*SearchW_full = 2*SearchW + 1; */ /* the full searching window size */ - SimilW_full = 2*SimilW + 1; /* the full similarity window size */ - h2 = h*h; - denh2 = 1/(2*h2); - - /*Gaussian kernel */ - Eucl_Vec = (float*) calloc (SimilW_full*SimilW_full*SimilW_full,sizeof(float)); - 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++) { - Eucl_Vec[count] = exp(-(pow((float)i_n, 2) + pow((float)j_n, 2) + pow((float)k_n, 2))/(2*SimilW*SimilW*SimilW)); - count = count + 1; - }}} /*main neighb loop */ - - /*The NLM code starts here*/ - /* setting OMP here */ - #pragma omp parallel for shared (A, B, dimX, dimY, dimZ, Eucl_Vec, lambda, denh2) private(denom, i, j, k, WeightGlob,count, i1, j1, k1, i2, j2, k2, i3, j3, k3, i5, j5, k5, Weight_norm, normsum, i_m, j_m, k_m, i_n, j_n, k_n, i_l, j_l, k_l, i_p, j_p, k_p, Weight, value) - for(i=0; i= padXY) && (i < dimX-padXY)) && ((j >= padXY) && (j < dimY-padXY)) && ((k >= padXY) && (k < dimZ-padXY))) { - /* take all elements around the pixel of interest */ - /* Massive Search window loop */ - Weight_norm = 0; value = 0.0; - for(i_m=-SearchW; i_m<=SearchW; i_m++) { - for(j_m=-SearchW; j_m<=SearchW; j_m++) { - for(k_m=-SearchW; k_m<=SearchW; k_m++) { - /*checking boundaries*/ - i1 = i+i_m; j1 = j+j_m; k1 = k+k_m; - - WeightGlob = 0.0; - /* if inside the searching window */ - for(i_l=-SimilW; i_l<=SimilW; i_l++) { - for(j_l=-SimilW; j_l<=SimilW; j_l++) { - for(k_l=-SimilW; k_l<=SimilW; k_l++) { - i2 = i1+i_l; j2 = j1+j_l; k2 = k1+k_l; - - i3 = i+i_l; j3 = j+j_l; k3 = k+k_l; /*coordinates of the inner patch loop */ - - count = 0; normsum = 0.0; - for(i_p=-SimilW; i_p<=SimilW; i_p++) { - for(j_p=-SimilW; j_p<=SimilW; j_p++) { - for(k_p=-SimilW; k_p<=SimilW; k_p++) { - i5 = i2 + i_p; j5 = j2 + j_p; k5 = k2 + k_p; - normsum = normsum + Eucl_Vec[count]*pow(A[(dimX*dimY)*(k3+k_p)+(i3+i_p)*dimY+(j3+j_p)]-A[(dimX*dimY)*k5 + i5*dimY+j5], 2); - count = count + 1; - }}} - if (normsum != 0) Weight = (exp(-normsum*denh2)); - else Weight = 0.0; - WeightGlob += Weight; - }}} - value += A[(dimX*dimY)*k1 + i1*dimY+j1]*WeightGlob; - Weight_norm += WeightGlob; - - }}} /*search window loop end*/ - - /* the final loop to average all values in searching window with weights */ - denom = 1 + lambda*Weight_norm; - B[(dimX*dimY)*k + i*dimY+j] = (A[(dimX*dimY)*k + i*dimY+j] + lambda*value)/denom; - } - }}} /*main loop*/ - free(Eucl_Vec); - return *B; -} - -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 +} \ No newline at end of file diff --git a/main_func/regularizers_CPU/PatchBased_Regul_core.c b/main_func/regularizers_CPU/PatchBased_Regul_core.c new file mode 100644 index 0000000..6f0a48d --- /dev/null +++ b/main_func/regularizers_CPU/PatchBased_Regul_core.c @@ -0,0 +1,220 @@ +/* +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 Kazanteev +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 "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 (mandatory): +* 1. Image (2D or 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 (N x N)/volume (N x N x N) +* +* 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 = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); +* +* Please see more tests in a file: +TestTemporalSmoothing.m + +* +* Matlab + C/mex compilers needed +* to compile with OMP support: mex PB_Regul_CPU.c CFLAGS="\$CFLAGS -fopenmp -Wall" LDFLAGS="\$LDFLAGS -fopenmp" +* +* D. Kazantsev * +* 02/07/2014 +* Harwell, UK +*/ + +/*2D version*/ +float PB_FUNC2D(float *A, float *B, int dimX, int dimY, int padXY, int SearchW, int SimilW, float h, float lambda) +{ + int i, j, i_n, j_n, i_m, j_m, i_p, j_p, i_l, j_l, i1, j1, i2, j2, i3, j3, i5,j5, count, SimilW_full; + float *Eucl_Vec, h2, denh2, normsum, Weight, Weight_norm, value, denom, WeightGlob, t1; + + /*SearchW_full = 2*SearchW + 1; */ /* the full searching window size */ + SimilW_full = 2*SimilW + 1; /* the full similarity window size */ + h2 = h*h; + denh2 = 1/(2*h2); + + /*Gaussian kernel */ + Eucl_Vec = (float*) calloc (SimilW_full*SimilW_full,sizeof(float)); + count = 0; + for(i_n=-SimilW; i_n<=SimilW; i_n++) { + for(j_n=-SimilW; j_n<=SimilW; j_n++) { + t1 = pow(((float)i_n), 2) + pow(((float)j_n), 2); + Eucl_Vec[count] = exp(-(t1)/(2*SimilW*SimilW)); + count = count + 1; + }} /*main neighb loop */ + + /*The NLM code starts here*/ + /* setting OMP here */ + #pragma omp parallel for shared (A, B, dimX, dimY, Eucl_Vec, lambda, denh2) private(denom, i, j, WeightGlob, count, i1, j1, i2, j2, i3, j3, i5, j5, Weight_norm, normsum, i_m, j_m, i_n, j_n, i_l, j_l, i_p, j_p, Weight, value) + + for(i=0; i= padXY) && (i < dimX-padXY)) && ((j >= padXY) && (j < dimY-padXY))) { + + /* Massive Search window loop */ + Weight_norm = 0; value = 0.0; + for(i_m=-SearchW; i_m<=SearchW; i_m++) { + for(j_m=-SearchW; j_m<=SearchW; j_m++) { + /*checking boundaries*/ + i1 = i+i_m; j1 = j+j_m; + + WeightGlob = 0.0; + /* if inside the searching window */ + for(i_l=-SimilW; i_l<=SimilW; i_l++) { + for(j_l=-SimilW; j_l<=SimilW; j_l++) { + i2 = i1+i_l; j2 = j1+j_l; + + i3 = i+i_l; j3 = j+j_l; /*coordinates of the inner patch loop */ + + count = 0; normsum = 0.0; + for(i_p=-SimilW; i_p<=SimilW; i_p++) { + for(j_p=-SimilW; j_p<=SimilW; j_p++) { + i5 = i2 + i_p; j5 = j2 + j_p; + normsum = normsum + Eucl_Vec[count]*pow(A[(i3+i_p)*dimY+(j3+j_p)]-A[i5*dimY+j5], 2); + count = count + 1; + }} + if (normsum != 0) Weight = (exp(-normsum*denh2)); + else Weight = 0.0; + WeightGlob += Weight; + }} + + value += A[i1*dimY+j1]*WeightGlob; + Weight_norm += WeightGlob; + }} /*search window loop end*/ + + /* the final loop to average all values in searching window with weights */ + denom = 1 + lambda*Weight_norm; + B[i*dimY+j] = (A[i*dimY+j] + lambda*value)/denom; + } + }} /*main loop*/ + return (*B); + free(Eucl_Vec); +} + +/*3D version*/ + float PB_FUNC3D(float *A, float *B, int dimX, int dimY, int dimZ, int padXY, int SearchW, int SimilW, float h, float lambda) + { + int SimilW_full, count, i, j, k, i_n, j_n, k_n, i_m, j_m, k_m, i_p, j_p, k_p, i_l, j_l, k_l, i1, j1, k1, i2, j2, k2, i3, j3, k3, i5, j5, k5; + float *Eucl_Vec, h2, denh2, normsum, Weight, Weight_norm, value, denom, WeightGlob; + + /*SearchW_full = 2*SearchW + 1; */ /* the full searching window size */ + SimilW_full = 2*SimilW + 1; /* the full similarity window size */ + h2 = h*h; + denh2 = 1/(2*h2); + + /*Gaussian kernel */ + Eucl_Vec = (float*) calloc (SimilW_full*SimilW_full*SimilW_full,sizeof(float)); + 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++) { + Eucl_Vec[count] = exp(-(pow((float)i_n, 2) + pow((float)j_n, 2) + pow((float)k_n, 2))/(2*SimilW*SimilW*SimilW)); + count = count + 1; + }}} /*main neighb loop */ + + /*The NLM code starts here*/ + /* setting OMP here */ + #pragma omp parallel for shared (A, B, dimX, dimY, dimZ, Eucl_Vec, lambda, denh2) private(denom, i, j, k, WeightGlob,count, i1, j1, k1, i2, j2, k2, i3, j3, k3, i5, j5, k5, Weight_norm, normsum, i_m, j_m, k_m, i_n, j_n, k_n, i_l, j_l, k_l, i_p, j_p, k_p, Weight, value) + for(i=0; i= padXY) && (i < dimX-padXY)) && ((j >= padXY) && (j < dimY-padXY)) && ((k >= padXY) && (k < dimZ-padXY))) { + /* take all elements around the pixel of interest */ + /* Massive Search window loop */ + Weight_norm = 0; value = 0.0; + for(i_m=-SearchW; i_m<=SearchW; i_m++) { + for(j_m=-SearchW; j_m<=SearchW; j_m++) { + for(k_m=-SearchW; k_m<=SearchW; k_m++) { + /*checking boundaries*/ + i1 = i+i_m; j1 = j+j_m; k1 = k+k_m; + + WeightGlob = 0.0; + /* if inside the searching window */ + for(i_l=-SimilW; i_l<=SimilW; i_l++) { + for(j_l=-SimilW; j_l<=SimilW; j_l++) { + for(k_l=-SimilW; k_l<=SimilW; k_l++) { + i2 = i1+i_l; j2 = j1+j_l; k2 = k1+k_l; + + i3 = i+i_l; j3 = j+j_l; k3 = k+k_l; /*coordinates of the inner patch loop */ + + count = 0; normsum = 0.0; + for(i_p=-SimilW; i_p<=SimilW; i_p++) { + for(j_p=-SimilW; j_p<=SimilW; j_p++) { + for(k_p=-SimilW; k_p<=SimilW; k_p++) { + i5 = i2 + i_p; j5 = j2 + j_p; k5 = k2 + k_p; + normsum = normsum + Eucl_Vec[count]*pow(A[(dimX*dimY)*(k3+k_p)+(i3+i_p)*dimY+(j3+j_p)]-A[(dimX*dimY)*k5 + i5*dimY+j5], 2); + count = count + 1; + }}} + if (normsum != 0) Weight = (exp(-normsum*denh2)); + else Weight = 0.0; + WeightGlob += Weight; + }}} + value += A[(dimX*dimY)*k1 + i1*dimY+j1]*WeightGlob; + Weight_norm += WeightGlob; + + }}} /*search window loop end*/ + + /* the final loop to average all values in searching window with weights */ + denom = 1 + lambda*Weight_norm; + B[(dimX*dimY)*k + i*dimY+j] = (A[(dimX*dimY)*k + i*dimY+j] + lambda*value)/denom; + } + }}} /*main loop*/ + free(Eucl_Vec); + return *B; +} + +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/main_func/regularizers_CPU/PatchBased_Regul_core.h b/main_func/regularizers_CPU/PatchBased_Regul_core.h new file mode 100644 index 0000000..b83cf10 --- /dev/null +++ b/main_func/regularizers_CPU/PatchBased_Regul_core.h @@ -0,0 +1,64 @@ +/* +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 Kazanteev +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. +*/ + +#define _USE_MATH_DEFINES + +#include +#include +#include +#include +#include +#include "omp.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 (mandatory): +* 1. Image (2D or 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 (N x N)/volume (N x N x N) +* +* 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 = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); +* +* Please see more tests in a file: +TestTemporalSmoothing.m + +* +* Matlab + C/mex compilers needed +* to compile with OMP support: mex PB_Regul_CPU.c CFLAGS="\$CFLAGS -fopenmp -Wall" LDFLAGS="\$LDFLAGS -fopenmp" +* +* D. Kazantsev * +* 02/07/2014 +* Harwell, 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); +float PB_FUNC2D(float *A, float *B, int dimX, int dimY, int padXY, int SearchW, int SimilW, float h, float lambda); +float PB_FUNC3D(float *A, float *B, int dimX, int dimY, int dimZ, int padXY, int SearchW, int SimilW, float h, float lambda); diff --git a/main_func/regularizers_CPU/SplitBregman_TV.c b/main_func/regularizers_CPU/SplitBregman_TV.c index f143aa6..0dc638d 100644 --- a/main_func/regularizers_CPU/SplitBregman_TV.c +++ b/main_func/regularizers_CPU/SplitBregman_TV.c @@ -1,10 +1,24 @@ +/* +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 Kazanteev +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 -#include -#include -#include -#include -#include "omp.h" +#include "SplitBregman_TV_core.h" /* C-OMP implementation of Split Bregman - TV denoising-regularization model (2D/3D) * @@ -30,16 +44,6 @@ * D. Kazantsev, 2016* */ -float copyIm(float *A, float *B, int dimX, int dimY, int dimZ); -float gauss_seidel2D(float *U, float *A, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY, float lambda, float mu); -float updDxDy_shrinkAniso2D(float *U, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY, float lambda); -float updDxDy_shrinkIso2D(float *U, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY, float lambda); -float updBxBy2D(float *U, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY); - -float gauss_seidel3D(float *U, float *A, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ, float lambda, float mu); -float updDxDyDz_shrinkAniso3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ, float lambda); -float updDxDyDz_shrinkIso3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ, float lambda); -float updBxByBz3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ); void mexFunction( int nlhs, mxArray *plhs[], @@ -171,229 +175,4 @@ void mexFunction( } printf("SB iterations stopped at iteration: %i\n", ll); } -} - -/* 2D-case related Functions */ -/*****************************************************************/ -float gauss_seidel2D(float *U, float *A, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY, float lambda, float mu) -{ - float sum, normConst; - int i,j,i1,i2,j1,j2; - normConst = 1.0f/(mu + 4.0f*lambda); - -#pragma omp parallel for shared(U) private(i,j,i1,i2,j1,j2,sum) - for(i=0; i +#include +#include +#include +#include +#include "omp.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* +*/ + +float copyIm(float *A, float *B, int dimX, int dimY, int dimZ); +float gauss_seidel2D(float *U, float *A, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY, float lambda, float mu); +float updDxDy_shrinkAniso2D(float *U, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY, float lambda); +float updDxDy_shrinkIso2D(float *U, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY, float lambda); +float updBxBy2D(float *U, float *Dx, float *Dy, float *Bx, float *By, int dimX, int dimY); + +float gauss_seidel3D(float *U, float *A, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ, float lambda, float mu); +float updDxDyDz_shrinkAniso3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ, float lambda); +float updDxDyDz_shrinkIso3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ, float lambda); +float updBxByBz3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int dimX, int dimY, int dimZ); diff --git a/main_func/regularizers_CPU/TGV_PD.c b/main_func/regularizers_CPU/TGV_PD.c index 41f8615..8bce18a 100644 --- a/main_func/regularizers_CPU/TGV_PD.c +++ b/main_func/regularizers_CPU/TGV_PD.c @@ -1,10 +1,24 @@ +/* +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 Kazanteev +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" -#include -#include -#include -#include -#include -#include "omp.h" /* C-OMP implementation of Primal-Dual denoising method for * Total Generilized Variation (TGV)-L2 model (2D case only) @@ -32,19 +46,6 @@ * 28.11.16/Harwell */ -/* 2D functions */ -float DualP_2D(float *U, float *V1, float *V2, float *P1, float *P2, int dimX, int dimY, int dimZ, float sigma); -float ProjP_2D(float *P1, float *P2, int dimX, int dimY, int dimZ, float alpha1); -float DualQ_2D(float *V1, float *V2, float *Q1, float *Q2, float *Q3, int dimX, int dimY, int dimZ, float sigma); -float ProjQ_2D(float *Q1, float *Q2, float *Q3, int dimX, int dimY, int dimZ, float alpha0); -float DivProjP_2D(float *U, float *A, float *P1, float *P2, int dimX, int dimY, int dimZ, float lambda, float tau); -float UpdV_2D(float *V1, float *V2, float *P1, float *P2, float *Q1, float *Q2, float *Q3, int dimX, int dimY, int dimZ, float tau); -/*3D functions*/ -float DualP_3D(float *U, float *V1, float *V2, float *V3, float *P1, float *P2, float *P3, int dimX, int dimY, int dimZ, float sigma); - -float newU(float *U, float *U_old, int dimX, int dimY, int dimZ); -float copyIm(float *A, float *U, int dimX, int dimY, int dimZ); - void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) @@ -183,171 +184,3 @@ void mexFunction( // } /*end of iterations*/ // } } - -/*Calculating dual variable P (using forward differences)*/ -float DualP_2D(float *U, float *V1, float *V2, float *P1, float *P2, int dimX, int dimY, int dimZ, float sigma) -{ - int i,j; -#pragma omp parallel for shared(U,V1,V2,P1,P2) private(i,j) - for(i=0; i 1.0) { - P1[i*dimY + (j)] = P1[i*dimY + (j)]/grad_magn; - P2[i*dimY + (j)] = P2[i*dimY + (j)]/grad_magn; - } - }} - return 1; -} -/*Calculating dual variable Q (using forward differences)*/ -float DualQ_2D(float *V1, float *V2, float *Q1, float *Q2, float *Q3, int dimX, int dimY, int dimZ, float sigma) -{ - int i,j; - float q1, q2, q11, q22; -#pragma omp parallel for shared(Q1,Q2,Q3,V1,V2) private(i,j,q1,q2,q11,q22) - for(i=0; i 1.0) { - Q1[i*dimY + (j)] = Q1[i*dimY + (j)]/grad_magn; - Q2[i*dimY + (j)] = Q2[i*dimY + (j)]/grad_magn; - Q3[i*dimY + (j)] = Q3[i*dimY + (j)]/grad_magn; - } - }} - return 1; -} -/* Divergence and projection for P*/ -float DivProjP_2D(float *U, float *A, float *P1, float *P2, int dimX, int dimY, int dimZ, float lambda, float tau) -{ - int i,j; - float P_v1, P_v2, div; -#pragma omp parallel for shared(U,A,P1,P2) private(i,j,P_v1,P_v2,div) - for(i=0; i 1.0) { + P1[i*dimY + (j)] = P1[i*dimY + (j)]/grad_magn; + P2[i*dimY + (j)] = P2[i*dimY + (j)]/grad_magn; + } + }} + return 1; +} +/*Calculating dual variable Q (using forward differences)*/ +float DualQ_2D(float *V1, float *V2, float *Q1, float *Q2, float *Q3, int dimX, int dimY, int dimZ, float sigma) +{ + int i,j; + float q1, q2, q11, q22; +#pragma omp parallel for shared(Q1,Q2,Q3,V1,V2) private(i,j,q1,q2,q11,q22) + for(i=0; i 1.0) { + Q1[i*dimY + (j)] = Q1[i*dimY + (j)]/grad_magn; + Q2[i*dimY + (j)] = Q2[i*dimY + (j)]/grad_magn; + Q3[i*dimY + (j)] = Q3[i*dimY + (j)]/grad_magn; + } + }} + return 1; +} +/* Divergence and projection for P*/ +float DivProjP_2D(float *U, float *A, float *P1, float *P2, int dimX, int dimY, int dimZ, float lambda, float tau) +{ + int i,j; + float P_v1, P_v2, div; +#pragma omp parallel for shared(U,A,P1,P2) private(i,j,P_v1,P_v2,div) + for(i=0; i +#include +#include +#include +#include +#include "omp.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 = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550); toc; +* +* to compile with OMP support: mex TGV_PD.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +* References: +* K. Bredies "Total Generalized Variation" +* +* 28.11.16/Harwell +*/ + +/* 2D functions */ +float DualP_2D(float *U, float *V1, float *V2, float *P1, float *P2, int dimX, int dimY, int dimZ, float sigma); +float ProjP_2D(float *P1, float *P2, int dimX, int dimY, int dimZ, float alpha1); +float DualQ_2D(float *V1, float *V2, float *Q1, float *Q2, float *Q3, int dimX, int dimY, int dimZ, float sigma); +float ProjQ_2D(float *Q1, float *Q2, float *Q3, int dimX, int dimY, int dimZ, float alpha0); +float DivProjP_2D(float *U, float *A, float *P1, float *P2, int dimX, int dimY, int dimZ, float lambda, float tau); +float UpdV_2D(float *V1, float *V2, float *P1, float *P2, float *Q1, float *Q2, float *Q3, int dimX, int dimY, int dimZ, float tau); +/*3D functions*/ +float DualP_3D(float *U, float *V1, float *V2, float *V3, float *P1, float *P2, float *P3, int dimX, int dimY, int dimZ, float sigma); + +float newU(float *U, float *U_old, int dimX, int dimY, int dimZ); +float copyIm(float *A, float *U, int dimX, int dimY, int dimZ); -- cgit v1.2.3