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
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
|
% Volume (3D) denoising demo using CCPi-RGL
clear; close all
Path1 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'mex_compile' filesep 'installed'], 1i);
Path2 = sprintf(['data' filesep], 1i);
Path3 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'supp'], 1i);
addpath(Path1);
addpath(Path2);
addpath(Path3);
N = 512;
slices = 15;
vol3D = zeros(N,N,slices, 'single');
Ideal3D = zeros(N,N,slices, 'single');
Im = double(imread('lena_gray_512.tif'))/255; % loading image
for i = 1:slices
vol3D(:,:,i) = Im + .05*randn(size(Im));
Ideal3D(:,:,i) = Im;
end
vol3D(vol3D < 0) = 0;
figure; imshow(vol3D(:,:,7), [0 1]); title('Noisy image');
lambda_reg = 0.03; % regularsation parameter for all methods
%%
fprintf('Denoise a volume using the ROF-TV model (CPU) \n');
tau_rof = 0.0025; % time-marching constant
iter_rof = 300; % number of ROF iterations
tic; u_rof = ROF_TV(single(vol3D), lambda_reg, iter_rof, tau_rof); toc;
energyfunc_val_rof = TV_energy(single(u_rof),single(vol3D),lambda_reg, 1); % get energy function value
rmse_rof = (RMSE(Ideal3D(:),u_rof(:)));
fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rof);
figure; imshow(u_rof(:,:,7), [0 1]); title('ROF-TV denoised volume (CPU)');
%%
% fprintf('Denoise a volume using the ROF-TV model (GPU) \n');
% tau_rof = 0.0025; % time-marching constant
% iter_rof = 300; % number of ROF iterations
% tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_reg, iter_rof, tau_rof); toc;
% rmse_rofG = (RMSE(Ideal3D(:),u_rofG(:)));
% fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rofG);
% figure; imshow(u_rofG(:,:,7), [0 1]); title('ROF-TV denoised volume (GPU)');
%%
fprintf('Denoise a volume using the FGP-TV model (CPU) \n');
iter_fgp = 300; % number of FGP iterations
epsil_tol = 1.0e-05; % tolerance
tic; u_fgp = FGP_TV(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc;
energyfunc_val_fgp = TV_energy(single(u_fgp),single(vol3D),lambda_reg, 1); % get energy function value
rmse_fgp = (RMSE(Ideal3D(:),u_fgp(:)));
fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgp);
figure; imshow(u_fgp(:,:,7), [0 1]); title('FGP-TV denoised volume (CPU)');
%%
% fprintf('Denoise a volume using the FGP-TV model (GPU) \n');
% iter_fgp = 300; % number of FGP iterations
% epsil_tol = 1.0e-05; % tolerance
% tic; u_fgpG = FGP_TV_GPU(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc;
% rmse_fgpG = (RMSE(Ideal3D(:),u_fgpG(:)));
% fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgpG);
% figure; imshow(u_fgpG(:,:,7), [0 1]); title('FGP-TV denoised volume (GPU)');
%%
fprintf('Denoise a volume using the SB-TV model (CPU) \n');
iter_sb = 150; % number of SB iterations
epsil_tol = 1.0e-05; % tolerance
tic; u_sb = SB_TV(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc;
energyfunc_val_sb = TV_energy(single(u_sb),single(vol3D),lambda_reg, 1); % get energy function value
rmse_sb = (RMSE(Ideal3D(:),u_sb(:)));
fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmse_sb);
figure; imshow(u_sb(:,:,7), [0 1]); title('SB-TV denoised volume (CPU)');
%%
% fprintf('Denoise a volume using the SB-TV model (GPU) \n');
% iter_sb = 150; % number of SB iterations
% epsil_tol = 1.0e-05; % tolerance
% tic; u_sbG = SB_TV_GPU(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc;
% rmse_sbG = (RMSE(Ideal3D(:),u_sbG(:)));
% fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmse_sbG);
% figure; imshow(u_sbG(:,:,7), [0 1]); title('SB-TV denoised volume (GPU)');
%%
fprintf('Denoise a volume using the ROF-LLT model (CPU) \n');
lambda_ROF = lambda_reg; % ROF regularisation parameter
lambda_LLT = lambda_reg*0.35; % LLT regularisation parameter
iter_LLT = 300; % iterations
tau_rof_llt = 0.0025; % time-marching constant
tic; u_rof_llt = LLT_ROF(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc;
rmse_rof_llt = (RMSE(Ideal3D(:),u_rof_llt(:)));
fprintf('%s %f \n', 'RMSE error for ROF-LLT is:', rmse_rof_llt);
figure; imshow(u_rof_llt(:,:,7), [0 1]); title('ROF-LLT denoised volume (CPU)');
%%
% fprintf('Denoise a volume using the ROF-LLT model (GPU) \n');
% lambda_ROF = lambda_reg; % ROF regularisation parameter
% lambda_LLT = lambda_reg*0.35; % LLT regularisation parameter
% iter_LLT = 300; % iterations
% tau_rof_llt = 0.0025; % time-marching constant
% tic; u_rof_llt_g = LLT_ROF_GPU(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc;
% rmse_rof_llt = (RMSE(Ideal3D(:),u_rof_llt_g(:)));
% fprintf('%s %f \n', 'RMSE error for ROF-LLT is:', rmse_rof_llt);
% figure; imshow(u_rof_llt_g(:,:,7), [0 1]); title('ROF-LLT denoised volume (GPU)');
%%
fprintf('Denoise a volume using Nonlinear-Diffusion model (CPU) \n');
iter_diff = 300; % number of diffusion iterations
lambda_regDiff = 0.025; % regularisation for the diffusivity
sigmaPar = 0.015; % edge-preserving parameter
tau_param = 0.025; % time-marching constant
tic; u_diff = NonlDiff(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc;
rmse_diff = (RMSE(Ideal3D(:),u_diff(:)));
fprintf('%s %f \n', 'RMSE error for Diffusion is:', rmse_diff);
figure; imshow(u_diff(:,:,7), [0 1]); title('Diffusion denoised volume (CPU)');
%%
% fprintf('Denoise a volume using Nonlinear-Diffusion model (GPU) \n');
% iter_diff = 300; % number of diffusion iterations
% lambda_regDiff = 0.025; % regularisation for the diffusivity
% sigmaPar = 0.015; % edge-preserving parameter
% tau_param = 0.025; % time-marching constant
% tic; u_diff_g = NonlDiff_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc;
% rmse_diff = (RMSE(Ideal3D(:),u_diff_g(:)));
% fprintf('%s %f \n', 'RMSE error for Diffusion is:', rmse_diff);
% figure; imshow(u_diff_g(:,:,7), [0 1]); title('Diffusion denoised volume (GPU)');
%%
fprintf('Denoise using Fourth-order anisotropic diffusion model (CPU) \n');
iter_diff = 300; % number of diffusion iterations
lambda_regDiff = 3.5; % regularisation for the diffusivity
sigmaPar = 0.02; % edge-preserving parameter
tau_param = 0.0015; % time-marching constant
tic; u_diff4 = Diffusion_4thO(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc;
rmse_diff4 = (RMSE(Ideal3D(:),u_diff4(:)));
fprintf('%s %f \n', 'RMSE error for Anis.Diff of 4th order is:', rmse_diff4);
figure; imshow(u_diff4(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (CPU)');
%%
% fprintf('Denoise using Fourth-order anisotropic diffusion model (GPU) \n');
% iter_diff = 300; % number of diffusion iterations
% lambda_regDiff = 3.5; % regularisation for the diffusivity
% sigmaPar = 0.02; % edge-preserving parameter
% tau_param = 0.0015; % time-marching constant
% tic; u_diff4_g = Diffusion_4thO_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc;
% rmse_diff4 = (RMSE(Ideal3D(:),u_diff4_g(:)));
% fprintf('%s %f \n', 'RMSE error for Anis.Diff of 4th order is:', rmse_diff4);
% figure; imshow(u_diff4_g(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (GPU)');
%%
fprintf('Denoise using the TGV model (CPU) \n');
lambda_TGV = 0.03; % regularisation parameter
alpha1 = 1.0; % parameter to control the first-order term
alpha0 = 2.0; % parameter to control the second-order term
iter_TGV = 500; % number of Primal-Dual iterations for TGV
tic; u_tgv = TGV(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV); toc;
rmseTGV = RMSE(Ideal3D(:),u_tgv(:));
fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV);
figure; imshow(u_tgv(:,:,3), [0 1]); title('TGV denoised volume (CPU)');
%%
% fprintf('Denoise using the TGV model (GPU) \n');
% lambda_TGV = 0.03; % regularisation parameter
% alpha1 = 1.0; % parameter to control the first-order term
% alpha0 = 2.0; % parameter to control the second-order term
% iter_TGV = 500; % number of Primal-Dual iterations for TGV
% tic; u_tgv_gpu = TGV_GPU(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV); toc;
% rmseTGV = RMSE(Ideal3D(:),u_tgv_gpu(:));
% fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV);
% figure; imshow(u_tgv_gpu(:,:,3), [0 1]); title('TGV denoised volume (GPU)');
%%
%>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< %
fprintf('Denoise a volume using the FGP-dTV model (CPU) \n');
% create another volume (reference) with slightly less amount of noise
vol3D_ref = zeros(N,N,slices, 'single');
for i = 1:slices
vol3D_ref(:,:,i) = Im + .01*randn(size(Im));
end
vol3D_ref(vol3D_ref < 0) = 0;
% vol3D_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV)
iter_fgp = 300; % number of FGP iterations
epsil_tol = 1.0e-05; % tolerance
eta = 0.2; % Reference image gradient smoothing constant
tic; u_fgp_dtv = FGP_dTV(single(vol3D), single(vol3D_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc;
figure; imshow(u_fgp_dtv(:,:,7), [0 1]); title('FGP-dTV denoised volume (CPU)');
%%
fprintf('Denoise a volume using the FGP-dTV model (GPU) \n');
% create another volume (reference) with slightly less amount of noise
vol3D_ref = zeros(N,N,slices, 'single');
for i = 1:slices
vol3D_ref(:,:,i) = Im + .01*randn(size(Im));
end
vol3D_ref(vol3D_ref < 0) = 0;
% vol3D_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV)
iter_fgp = 300; % number of FGP iterations
epsil_tol = 1.0e-05; % tolerance
eta = 0.2; % Reference image gradient smoothing constant
tic; u_fgp_dtv_g = FGP_dTV_GPU(single(vol3D), single(vol3D_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc;
figure; imshow(u_fgp_dtv_g(:,:,7), [0 1]); title('FGP-dTV denoised volume (GPU)');
%%
|
