-
Notifications
You must be signed in to change notification settings - Fork 537
/
Demo_test_DnCNN3.m
337 lines (255 loc) · 13.4 KB
/
Demo_test_DnCNN3.m
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
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
%%% This is the testing demo for learning a single model for three tasks, including Gaussian denoing, SISR, JPEG image deblocking.
% clear; clc;
addpath('utilities');
%%% testing set
tasks = {'GD','SR','DB'}; %%% three tasks
imageSets = {'BSD68','Set5','Set14','BSD100','Urben100','classic5','LIVE1'}; %%% testing dataset
%%% setting
taskTest = tasks([1 2 3]); %%% choose the tasks for evaluation
setTest = {imageSets([1]),imageSets([2:5]),imageSets([6 7])}; %%% select the datasets for each tasks
showResult = [1 1 1]; %%% save the restored images
pauseTime = 1;
folderModel = 'model';
useGPU = 1; % 1 or 0, true or false
folderTest = 'testsets';
folderResult= 'results';
if ~exist(folderResult,'file')
mkdir(folderResult);
end
%%% task GD = Gaussian Denoising
sigma = 25;
%%% task SR = Single Image Super-Resolution
scale = 3;
%%% task DB = DeBlocking
Q = 20;
%%% load DnCNN-3 model
load(fullfile(folderModel,'DnCNN3.mat'));
%net = vl_simplenn_tidy(net);
% for i = 1:size(net.layers,2)
% net.layers{i}.precious = 1;
% end
if useGPU
net = vl_simplenn_move(net, 'gpu') ;
end
%%% input (single); output (single); label (ground-truth, uint8)
%%% input_RGB (uint8); output_RGB (uint8); label_RGB (ground-truth, uint8)
%%%-------------------------------------------------------------------------------------
%%% Gaussian Denoising (GD)
%%%-------------------------------------------------------------------------------------
if ismember('GD',taskTest)
taskTestCur = 'GD';
for n_set = 1 : numel(setTest{1})
%%% read images
setTestCur = cell2mat(setTest{1}(n_set));
disp('-----------------------------------------------');
disp(['----',setTestCur,'------Gaussian Denoising-----']);
disp('-----------------------------------------------');
folderTestCur = fullfile(folderTest,setTestCur);
ext = {'*.jpg','*.png','*.bmp'};
filepaths = [];
for i = 1 : length(ext)
filepaths = cat(1,filepaths,dir(fullfile(folderTestCur, ext{i})));
end
eval(['PSNR_',taskTestCur,'_',setTestCur,'_s',num2str(sigma),' = zeros(length(filepaths),1);']);
eval(['SSIM_',taskTestCur,'_',setTestCur,'_s',num2str(sigma),' = zeros(length(filepaths),1);']);
%%% folder to store results
folderResultCur = fullfile(folderResult, [taskTestCur,'_',setTestCur,'_s',num2str(sigma)]);
if ~exist(folderResultCur,'file')
mkdir(folderResultCur);
end
for i = 1 : length(filepaths)
label = imread(fullfile(folderTestCur,filepaths(i).name));
[~,imageName,ext] = fileparts(filepaths(i).name);
chanel = size(label,3);
if chanel == 3
%%% label (uint8)
label = rgb2gray(label);
end
%%% input (single)
randn('seed',0);
input = single(im2double(label) + sigma/255*randn(size(label)));
if useGPU
input = gpuArray(input);
end
res = vl_simplenn(net, input,[],[],'conserveMemory',true,'mode','test');
im = res(end).x;
%%% output (single)
output = gather(input - im);
[PSNR_Cur,SSIM_Cur] = Cal_PSNRSSIM(label,im2uint8(output),0,0);
disp(['Denoising ',num2str(PSNR_Cur,'%2.2f'),'dB',' ',filepaths(i).name]);
eval(['PSNR_',taskTestCur,'_',setTestCur,'_s',num2str(sigma),'(',num2str(i),') = PSNR_Cur;']);
eval(['SSIM_',taskTestCur,'_',setTestCur,'_s',num2str(sigma),'(',num2str(i),') = SSIM_Cur;']);
if showResult(1)
imshow(cat(1,cat(2,im2uint8(input),im2uint8(output)),cat(2,im2uint8(abs(input-output)*10),label)));
drawnow;
title(['Denoising ',filepaths(i).name,' ',num2str(PSNR_Cur,'%2.2f'),'dB'],'FontSize',12)
pause(pauseTime)
%pause()
%%% save results
imwrite(output,fullfile(folderResultCur,[imageName,'_s',num2str(sigma),'.png']));
end
end
disp(['Average PSNR is ',num2str(mean(eval(['PSNR_',taskTestCur,'_',setTestCur,'_s',num2str(sigma)])),'%2.2f'),'dB']);
disp(['Average SSIM is ',num2str(mean(eval(['SSIM_',taskTestCur,'_',setTestCur,'_s',num2str(sigma)])),'%2.4f')]);
%%% save PSNR and SSIM metrics
save(fullfile(folderResultCur,['PSNR_',taskTestCur,'_',setTestCur,'_s',num2str(sigma),'.mat']),['PSNR_',taskTestCur,'_',setTestCur,'_s',num2str(sigma)])
save(fullfile(folderResultCur,['SSIM_',taskTestCur,'_',setTestCur,'_s',num2str(sigma),'.mat']),['SSIM_',taskTestCur,'_',setTestCur,'_s',num2str(sigma)])
end
end
%%%-------------------------------------------------------------------------------------
%%% Single Image Super-Resolution (SR)
%%%-------------------------------------------------------------------------------------
if ismember('SR',taskTest)
taskTestCur = 'SR';
for n_set = 1 : numel(setTest{2})
%%% read images
setTestCur = cell2mat(setTest{2}(n_set));
disp('--------------------------------------------');
disp(['----',setTestCur,'-----Super-Resolution-----']);
disp('--------------------------------------------');
folderTestCur = fullfile(folderTest,setTestCur);
ext = {'*.jpg','*.png','*.bmp'};
filepaths = [];
for i = 1 : length(ext)
filepaths = cat(1,filepaths,dir(fullfile(folderTestCur, ext{i})));
end
eval(['PSNR_',taskTestCur,'_',setTestCur,'_x',num2str(scale),' = zeros(length(filepaths),1);']);
eval(['SSIM_',taskTestCur,'_',setTestCur,'_x',num2str(scale),' = zeros(length(filepaths),1);']);
if fix(scale) == scale
crop = scale;
else
crop = scale*10;
end
%%% folder to store results
folderResultCur = fullfile(folderResult, [taskTestCur,'_',setTestCur,'_x',num2str(scale)]);
if ~exist(folderResultCur,'file')
mkdir(folderResultCur);
end
for i = 1 : length(filepaths)
HR = imread(fullfile(folderTestCur,filepaths(i).name));
[~,imageName,ext] = fileparts(filepaths(i).name);
HR = modcrop(HR, crop);
%%% label_RGB (uint8)
label_RGB = HR;
chanel = size(HR,3);
%%% LR (uint8)
LR = imresize(HR,1/scale,'bicubic');
if chanel == 3
%%% label (single)
HR_ycc = single(rgb2ycbcr(im2double(HR)));
label = HR_ycc(:,:,1);
%%% input (single)
HR_bic = imresize(im2double(LR),scale,'bicubic');
LR_bic_ycc = rgb2ycbcr(HR_bic);
input = im2single(LR_bic_ycc(:,:,1));
%%% input_RGB (uint8)
input_RGB = im2uint8(HR_bic);
else
%%% label (single)
label = im2single(HR);
HR_bic = imresize(LR,scale,'bicubic');
%%% input (single)
input = im2single(HR_bic);
%%% input_RGB (uint8)
input_RGB = HR_bic;
end
if useGPU
input = gpuArray(input);
end
res = vl_simplenn(net, input,[],[],'conserveMemory',true,'mode','test');
im = res(end).x;
%%% output (single)
output = gather(input - im);
if chanel == 3
%%% output_RGB (uint8)
LR_bic_ycc(:,:,1) = double(output);
output_RGB = im2uint8(ycbcr2rgb(LR_bic_ycc));
else
%%% output_RGB (uint8)
output_RGB = im2uint8(output);
end
[PSNR_Cur,SSIM_Cur] = Cal_PSNRSSIM(label*255,output*255,ceil(scale),ceil(scale)); %%% single
disp(['Single Image Super-Resolution ',num2str(PSNR_Cur,'%2.2f'),'dB',' ',filepaths(i).name]);
eval(['PSNR_SR_',setTestCur,'_x',num2str(scale),'(',num2str(i),') = PSNR_Cur;']);
eval(['SSIM_SR_',setTestCur,'_x',num2str(scale),'(',num2str(i),') = SSIM_Cur;']);
if showResult(2)
imshow(cat(1,cat(2,input_RGB,output_RGB),cat(2,(output_RGB-input_RGB),label_RGB)));
drawnow;
title(['Single Image Super-Resolution ',filepaths(i).name,' ',num2str(PSNR_Cur,'%2.2f'),'dB'],'FontSize',12)
pause(pauseTime)
% pause()
%%% save results
imwrite(output_RGB,fullfile(folderResultCur,[imageName,'_x',num2str(scale),'.png']));
end
end
disp(['Average PSNR is ',num2str(mean(eval(['PSNR_',taskTestCur,'_',setTestCur,'_x',num2str(scale)])),'%2.2f'),'dB']);
disp(['Average SSIM is ',num2str(mean(eval(['SSIM_',taskTestCur,'_',setTestCur,'_x',num2str(scale)])),'%2.4f')]);
%%% save PSNR and SSIM metrics
save(fullfile(folderResultCur,['PSNR_',taskTestCur,'_',setTestCur,'_x',num2str(scale),'.mat']),['PSNR_',taskTestCur,'_',setTestCur,'_x',num2str(scale)])
save(fullfile(folderResultCur,['SSIM_',taskTestCur,'_',setTestCur,'_x',num2str(scale),'.mat']),['SSIM_',taskTestCur,'_',setTestCur,'_x',num2str(scale)])
end
end
%%%-------------------------------------------------------------------------------------
%%% JPEG Image Deblocking (DB)
%%%-------------------------------------------------------------------------------------
if ismember('DB',taskTest)
taskTestCur = 'DB';
for n_set = 1 : numel(setTest{3})
%%% read image names
setTestCur = cell2mat(setTest{3}(n_set));
disp('---------------------------------------');
disp(['----',setTestCur,'------Deblocking-----']);
disp('---------------------------------------');
folderTestCur = fullfile(folderTest,setTestCur);
ext = {'*.jpg','*.png','*.bmp'};
filepaths = [];
for i = 1 : length(ext)
filepaths = cat(1,filepaths,dir(fullfile(folderTestCur, ext{i})));
end
%%% to store PSNR and SSIM results
eval(['PSNR_',taskTestCur,'_',setTestCur,'_q',num2str(Q),' = zeros(length(filepaths),1);']);
eval(['SSIM_',taskTestCur,'_',setTestCur,'_q',num2str(Q),' = zeros(length(filepaths),1);']);
%%% to store results
folderResultCur = fullfile(folderResult, [taskTestCur,'_',setTestCur,'_q',num2str(Q)]);
if ~exist(folderResultCur,'file')
mkdir(folderResultCur);
end
for i = 1 : length(filepaths)
label = imread(fullfile(folderTestCur,filepaths(i).name));
[~,imageName,ext] = fileparts(filepaths(i).name);
chanel = size(label,3);
if chanel == 3
%%% label (uint8)
label = rgb2ycbcr(label);
label = label(:,:,1);
end
%%% input (single)
imwrite(label,'test.jpg','jpg','quality',Q);
input = im2single(imread('test.jpg'));
if useGPU
input = gpuArray(input);
end
res = vl_simplenn(net, input,[],[],'conserveMemory',true,'mode','test');
im = res(end).x;
%%% output (single)
output = gather(input - im);
[PSNR_Cur,SSIM_Cur] = Cal_PSNRSSIM(label,im2uint8(output),0,0);
disp(['Deblocking ',num2str(PSNR_Cur,'%2.2f'),'dB',' ',filepaths(i).name]);
eval(['PSNR_',taskTestCur,'_',setTestCur,'_q',num2str(Q),'(',num2str(i),') = PSNR_Cur;']);
eval(['SSIM_',taskTestCur,'_',setTestCur,'_q',num2str(Q),'(',num2str(i),') = SSIM_Cur;']);
if showResult(3)
imshow(cat(1,cat(2,im2uint8(input),im2uint8(output)),cat(2,im2uint8(abs(input-output)*10),label)));
drawnow;
title(['Deblocking ',filepaths(i).name,' ',num2str(PSNR_Cur,'%2.2f'),'dB'],'FontSize',12)
pause(pauseTime)
%%% save results
imwrite(output,fullfile(folderResultCur,[imageName,'_q',num2str(Q),'.png']));
end
end
disp(['Average PSNR is ',num2str(mean(eval(['PSNR_',taskTestCur,'_',setTestCur,'_q',num2str(Q)])),'%2.2f'),'dB']);
disp(['Average SSIM is ',num2str(mean(eval(['SSIM_',taskTestCur,'_',setTestCur,'_q',num2str(Q)])),'%2.4f')]);
%%% save PSNR and SSIM metrics
save(fullfile(folderResultCur,['PSNR_',taskTestCur,'_',setTestCur,'_q',num2str(Q),'.mat']),['PSNR_',taskTestCur,'_',setTestCur,'_q',num2str(Q)])
save(fullfile(folderResultCur,['SSIM_',taskTestCur,'_',setTestCur,'_q',num2str(Q),'.mat']),['SSIM_',taskTestCur,'_',setTestCur,'_q',num2str(Q)])
end
end