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
|
/*
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 <math.h>
#include <stdlib.h>
#include <memory.h>
#include <stdio.h>
#include "omp.h"
#include "utils.h"
#include "CCPiDefines.h"
/* C-OMP implementation of FGP-dTV [1,2] denoising/regularization model (2D/3D case)
* which employs structural similarity of the level sets of two images/volumes, see [1,2]
* The current implementation updates image 1 while image 2 is being fixed.
*
* Input Parameters:
* 1. Noisy image/volume [REQUIRED]
* 2. Additional reference image/volume of the same dimensions as (1) [REQUIRED]
* 3. lambdaPar - regularization parameter [REQUIRED]
* 4. Number of iterations [OPTIONAL]
* 5. eplsilon: tolerance constant [OPTIONAL]
* 6. eta: smoothing constant to calculate gradient of the reference [OPTIONAL] *
* 7. TV-type: methodTV - 'iso' (0) or 'l1' (1) [OPTIONAL]
* 8. nonneg: 'nonnegativity (0 is OFF by default) [OPTIONAL]
* 9. print information: 0 (off) or 1 (on) [OPTIONAL]
*
* Output:
* [1] Filtered/regularized image/volume
* [2] Information vector which contains [iteration no., reached tolerance]
*
* This function is based on the Matlab's codes and papers by
* [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems"
* [2] M. J. Ehrhardt and M. M. Betcke, Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation, SIAM Journal on Imaging Sciences 9(3), pp. 1084–1106
*/
#ifdef __cplusplus
extern "C" {
#endif
CCPI_EXPORT float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int dimX, int dimY, int dimZ);
CCPI_EXPORT float GradNorm_func2D(float *B, float *B_x, float *B_y, float eta, long dimX, long dimY);
CCPI_EXPORT float ProjectVect_func2D(float *R1, float *R2, float *B_x, float *B_y, long dimX, long dimY);
CCPI_EXPORT float Obj_dfunc2D(float *A, float *D, float *R1, float *R2, float lambda, long dimX, long dimY);
CCPI_EXPORT float Grad_dfunc2D(float *P1, float *P2, float *D, float *R1, float *R2, float *B_x, float *B_y, float lambda, long dimX, long dimY);
CCPI_EXPORT float Rupd_dfunc2D(float *P1, float *P1_old, float *P2, float *P2_old, float *R1, float *R2, float tkp1, float tk, long DimTotal);
CCPI_EXPORT float GradNorm_func3D(float *B, float *B_x, float *B_y, float *B_z, float eta, long dimX, long dimY, long dimZ);
CCPI_EXPORT float ProjectVect_func3D(float *R1, float *R2, float *R3, float *B_x, float *B_y, float *B_z, long dimX, long dimY, long dimZ);
CCPI_EXPORT float Obj_dfunc3D(float *A, float *D, float *R1, float *R2, float *R3, float lambda, long dimX, long dimY, long dimZ);
CCPI_EXPORT float Grad_dfunc3D(float *P1, float *P2, float *P3, float *D, float *R1, float *R2, float *R3, float *B_x, float *B_y, float *B_z, float lambda, long dimX, long dimY, long dimZ);
CCPI_EXPORT float Rupd_dfunc3D(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, long DimTotal);
#ifdef __cplusplus
}
#endif
|
