1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
|
/*
* This work is part of the Core Imaging Library developed by
* Visual Analytics and Imaging System Group of the Science Technology
* Facilities Council, STFC and Diamond Light Source Ltd.
*
* Copyright 2017 Daniil Kazantsev
* Copyright 2017 Srikanth Nagella, Edoardo Pasca
* Copyright 2018 Diamond Light Source Ltd.
*
* 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 "Nonlocal_TV_core.h"
#define EPS 1.0000e-9
/* Matlab wrapper for C-OMP implementation of non-local regulariser
* Weights and associated indices must be given as an input.
* Gauss-Seidel fixed point iteration requires ~ 3 iterations, so the main effort
* goes in pre-calculation of weights and selection of patches
*
*
* Input Parameters:
* 1. 2D/3D grayscale image/volume
* 2. AR_i - indeces of i neighbours
* 3. AR_j - indeces of j neighbours
* 4. AR_k - indeces of k neighbours (0 - for 2D case)
* 5. Weights_ij(k) - associated weights
* 6. regularisation parameter
* 7. iterations number
* Output:
* 1. denoised image/volume
* Elmoataz, Abderrahim, Olivier Lezoray, and Sébastien Bougleux. "Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing." IEEE Trans. Image Processing 17, no. 7 (2008): 1047-1060.
*/
void mexFunction(
int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[])
{
long number_of_dims, dimX, dimY, dimZ;
int IterNumb, NumNeighb = 0;
unsigned short *H_i, *H_j, *H_k;
const mwSize *dim_array;
const mwSize *dim_array2;
float *A_orig, *Output=NULL, *Weights, lambda;
dim_array = mxGetDimensions(prhs[0]);
dim_array2 = mxGetDimensions(prhs[1]);
number_of_dims = mxGetNumberOfDimensions(prhs[0]);
/*Handling Matlab input data*/
A_orig = (float *) mxGetData(prhs[0]); /* a 2D image or a set of 2D images (3D stack) */
H_i = (unsigned short *) mxGetData(prhs[1]); /* indeces of i neighbours */
H_j = (unsigned short *) mxGetData(prhs[2]); /* indeces of j neighbours */
H_k = (unsigned short *) mxGetData(prhs[3]); /* indeces of k neighbours */
Weights = (float *) mxGetData(prhs[4]); /* weights for patches */
lambda = (float) mxGetScalar(prhs[5]); /* regularisation parameter */
IterNumb = (int) mxGetScalar(prhs[6]); /* the number of iterations */
dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
/*****2D INPUT *****/
if (number_of_dims == 2) {
dimZ = 0;
NumNeighb = dim_array2[2];
Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
}
/*****3D INPUT *****/
/****************************************************/
if (number_of_dims == 3) {
NumNeighb = dim_array2[3];
Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
}
/* run the main function here */
Nonlocal_TV_CPU_main(A_orig, Output, H_i, H_j, H_k, Weights, dimX, dimY, dimZ, NumNeighb, lambda, IterNumb, 0);
}
|
