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Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Matlab/mex_compile/regularisers_CPU/NeighbSearch2D_test.c | 207 |
1 files changed, 207 insertions, 0 deletions
diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/NeighbSearch2D_test.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/NeighbSearch2D_test.c new file mode 100644 index 0000000..d94b521 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/NeighbSearch2D_test.c @@ -0,0 +1,207 @@ +#include "mex.h" +#include <matrix.h> +#include <math.h> +#include <stdlib.h> +#include <memory.h> +#include <stdio.h> +#include "omp.h" + +#define EPS 1.0000e-12 + +/* C implementation of the spatial-dependent histogram + * currently not optimal memory-wise + * + * + * Input Parameters: + * 1. 2D grayscale image (N x N) + * 2. Number of histogram bins (M) + * 4. Similarity window (half-size) + * + * Output: + * 1. Filtered Image (N x N) + * + * + * compile from Matlab with: + * mex NLTV_SB_fast.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" + * + * Im = double(imread('barb.bmp'))/255; % loading image + * u0 = Im + .05*randn(size(Im)); u0(u0<0) = 0; % adding noise + * [Filtered, theta, I1, J1] = NLTV_SB_fast(single(u0), 7, 7, 20, 0.1); + * D. Kazantsev + */ + +float copyIm(float *A, float *B, int dimX, int dimY, int dimZ); + +/*2D functions */ +float Indeces2D(float *Aorig, unsigned short *H_i, unsigned short *H_j, float *Weights, int i, int j, int dimY, int dimX, int NumNeighb, int SearchWindow, int SimilarWin, float h2); +float NLM_ST_H1(float *Aorig, float *Output, unsigned short *H_i, unsigned short *H_j, float *Weights, int i, int j, int dimX, int dimY, int NumNeighb, float beta, int IterNumb); + + + +float denoise2D(float *Aorig, float *Output, unsigned short *H_i, unsigned short *H_j, float *Weights, int i, int j, int dimY, int dimX, int NumNeighb); +/**************************************************/ + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) +{ + int number_of_dims, i, j, k, dimX, dimY, dimZ, SearchWindow, SimilarWin, NumNeighb,kk; + unsigned short *H_i=NULL, *H_j=NULL; + const int *dim_array; + float *A, *Output, *Weights, h, h2, lambda; + int dim_array2[3]; + + dim_array = mxGetDimensions(prhs[0]); + number_of_dims = mxGetNumberOfDimensions(prhs[0]); + + /*Handling Matlab input data*/ + A = (float *) mxGetData(prhs[0]); /* a 2D image or a set of 2D images (3D stack) */ + SearchWindow = (int) mxGetScalar(prhs[1]); /* Large Searching window to find and cluster intensities */ + SimilarWin = (int) mxGetScalar(prhs[2]); /* Similarity window */ + NumNeighb = (int) mxGetScalar(prhs[3]); /* the total number of neighbours to take */ + h = (float) mxGetScalar(prhs[4]); /* NLM parameter */ + + h2 = h*h; + dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2]; + dim_array2[0] = dimX; dim_array2[1] = dimY; dim_array2[2] = NumNeighb; /* 2D case */ + + /*****2D INPUT *****/ + if (number_of_dims == 2) { + dimZ = 0; + H_i = (unsigned short*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array2, mxUINT16_CLASS, mxREAL)); + H_j = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(3, dim_array2, mxUINT16_CLASS, mxREAL)); + Weights = (float*)mxGetPr(plhs[2] = mxCreateNumericArray(3, dim_array2, mxSINGLE_CLASS, mxREAL)); + Output = (float*)mxGetPr(plhs[3] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + + /* for each pixel store indeces of the most similar neighbours (patches) */ +#pragma omp parallel for shared (A, Output, Weights, H_i, H_j) private(i,j) + for(i=0; i<dimX; i++) { + for(j=0; j<dimY; j++) { + Indeces2D(A, H_i, H_j, Weights, i, j, dimX, dimY, NumNeighb, SearchWindow, SimilarWin, h2); + // denoise2D(A, Output, H_i, H_j, Weights, i, j, dimX, dimY, NumNeighb); + NLM_ST_H1(A, Output, H_i, H_j, Weights, i, j, dimX, dimY, NumNeighb, 0.01f, 1); + }} + } + /*****3D INPUT *****/ + /****************************************************/ + if (number_of_dims == 3) { + } +} + + +float Indeces2D(float *Aorig, unsigned short *H_i, unsigned short *H_j, float *Weights, int i, int j, int dimY, int dimX, int NumNeighb, int SearchWindow, int SimilarWin, float h2) +{ + int i1, j1, i_m, j_m, i_c, j_c, i2, j2, i3, j3, k, counter, x, y; + float *Weight_Vec, normsum, temp; + unsigned short *ind_i, *ind_j, temp_i, temp_j; + + + Weight_Vec = (float*) calloc((2*SearchWindow + 1)*(2*SearchWindow + 1), sizeof(float)); + ind_i = (unsigned short*) calloc((2*SearchWindow + 1)*(2*SearchWindow + 1), sizeof(unsigned short)); + ind_j = (unsigned short*) calloc((2*SearchWindow + 1)*(2*SearchWindow + 1), sizeof(unsigned short)); + + counter = 0; + for(i_m=-SearchWindow; i_m<=SearchWindow; i_m++) { + for(j_m=-SearchWindow; j_m<=SearchWindow; j_m++) { + i1 = i+i_m; + j1 = j+j_m; + if (((i1 >= 0) && (i1 < dimX)) && ((j1 >= 0) && (j1 < dimY))) { + normsum = 0; + for(i_c=-SimilarWin; i_c<=SimilarWin; i_c++) { + for(j_c=-SimilarWin; j_c<=SimilarWin; j_c++) { + i2 = i1 + i_c; + j2 = j1 + j_c; + i3 = i + i_c; + j3 = j + j_c; + if (((i2 >= 0) && (i2 < dimX)) && ((j2 >= 0) && (j2 < dimY))) { + if (((i3 >= 0) && (i3 < dimX)) && ((j3 >= 0) && (j3 < dimY))) { + normsum += pow(Aorig[i3*dimY+j3] - Aorig[i2*dimY+j2], 2); + }} + }} + /* writing temporarily into vectors */ + if (normsum > EPS) Weight_Vec[counter] = exp(-normsum/h2); + ind_i[counter] = i1; + ind_j[counter] = j1; + counter ++; + } + }} + /* do sorting to choose the most prominent weights [LOW -> HIGH]*/ + /* and re-arrange indeces accordingly */ + for(x=0; x < counter; x++) { + for(y=0; y < counter - 1; y++) { + if(Weight_Vec[y] < Weight_Vec[y+1]) { + temp = Weight_Vec[y+1]; + temp_i = ind_i[y+1]; + temp_j = ind_j[y+1]; + Weight_Vec[y+1] = Weight_Vec[y]; + Weight_Vec[y] = temp; + ind_i[y+1] = ind_i[y]; + ind_i[y] = temp_i; + ind_j[y+1] = ind_j[y]; + ind_j[y] = temp_j; + }}} /*sorting loop end*/ + + // printf("%f %i %i \n", Weight_Vec[10], ind_i[10], ind_j[10]); + /*now select NumNeighb more prominent weights */ + for(x=0; x < NumNeighb; x++) { + H_i[(dimX*dimY*x) + i*dimY+j] = ind_i[x]; + H_j[(dimX*dimY*x) + i*dimY+j] = ind_j[x]; + Weights[(dimX*dimY*x) + i*dimY+j] = Weight_Vec[x]; + } + + free(ind_i); + free(ind_j); + free(Weight_Vec); + return 1; +} + +/* a test if NLM denoising works */ +float denoise2D(float *Aorig, float *Output, unsigned short *H_i, unsigned short *H_j, float *Weights, int i, int j, int dimY, int dimX, int NumNeighb) +{ + int x, i1, j1; + float value = 0.0f, normweight = 0.0f; + + for(x=0; x < NumNeighb; x++) { + i1 = (H_i[(dimX*dimY*x) + i*dimY+j]); + j1 = (H_j[(dimX*dimY*x) + i*dimY+j]); + value += Aorig[i1*dimY+j1]*Weights[(dimX*dimY*x) + i*dimY+j]; + normweight += Weights[(dimX*dimY*x) + i*dimY+j]; + } + if (normweight != 0) Output[i*dimY+j] = value/normweight; + else Output[i*dimY+j] = 0.0f; + + return *Output; +} + +/***********<<<<Main Function for ST NLM - H1 penalty>>>>**********/ +float NLM_ST_H1(float *Aorig, float *Output, unsigned short *H_i, unsigned short *H_j, float *Weights, int i, int j, int dimX, int dimY, int NumNeighb, float beta, int IterNumb) +{ + int x, i1, j1; + float value = 0.0f, normweight = 0.0f; + + for(x=0; x < NumNeighb; x++) { + i1 = (H_i[(dimX*dimY*x) + i*dimY+j]); + j1 = (H_j[(dimX*dimY*x) + i*dimY+j]); + value += Aorig[i1*dimY+j1]*Weights[(dimX*dimY*x) + i*dimY+j]; + normweight += Weights[(dimX*dimY*x) + i*dimY+j]; + } + +// if (normweight != 0) Output[i*dimY+j] = value/normweight; +// else Output[i*dimY+j] = 0.0f; + + Output[i*dimY+j] = (beta*Aorig[i*dimY+j] + value)/(beta + normweight); + return *Output; +} + + + +/* General Functions */ +/*****************************************************************/ +/* Copy Image */ +float copyIm(float *A, float *B, int dimX, int dimY, int dimZ) +{ + int j; +#pragma omp parallel for shared(A, B) private(j) + for(j=0; j<dimX*dimY*dimZ; j++) B[j] = A[j]; + return *B; +} |