-
Notifications
You must be signed in to change notification settings - Fork 129
/
Demo_AWGN_Color_Clip.m
132 lines (102 loc) · 4.23 KB
/
Demo_AWGN_Color_Clip.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
% This is the testing demo of FFDNet for denoising noisy color images corrupted by
% AWGN with clipping setting. The noisy input is 8-bit quantized.
%
% To run the code, you should install Matconvnet first. Alternatively, you can use the
% function `vl_ffdnet_matlab` to perform denoising without Matconvnet.
%
% "FFDNet: Toward a Fast and Flexible Solution for CNN based Image
% Denoising" 2018/03/23
% If you have any question, please feel free to contact with me.
% Kai Zhang (e-mail: [email protected])
% clear; clc;
format compact;
global sigmas; % input noise level or input noise level map
addpath(fullfile('utilities'));
folderModel = 'models';
folderTest = 'testsets';
folderResult= 'results';
imageSets = {'CBSD68','Kodak24','McMaster'}; % testing datasets
setTestCur = imageSets{1}; % current testing dataset
showResult = 1;
useGPU = 1;
pauseTime = 0;
imageNoiseSigma = 25; % image noise level, 25.5 is the default setting of imnoise( ,'gaussian')
inputNoiseSigma = 25; % input noise level
folderResultCur = fullfile(folderResult, [setTestCur,'_Clip_',num2str(imageNoiseSigma(1)),'_',num2str(inputNoiseSigma(1))]);
if ~isdir(folderResultCur)
mkdir(folderResultCur)
end
load(fullfile('models','FFDNet_Clip_color.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
% read images
ext = {'*.jpg','*.png','*.bmp','*.tif'};
filePaths = [];
for i = 1 : length(ext)
filePaths = cat(1,filePaths, dir(fullfile(folderTest,setTestCur,ext{i})));
end
% PSNR and SSIM
PSNRs = zeros(1,length(filePaths));
SSIMs = zeros(1,length(filePaths));
for i = 1:length(filePaths)
% read images
label = imread(fullfile(folderTest,setTestCur,filePaths(i).name));
[w,h,c] = size(label);
if c == 3
[~,nameCur,extCur] = fileparts(filePaths(i).name);
label = im2single(label);
% add noise
randn('seed',0);
%input = imnoise(label,'gaussian'); % corresponds to imageNoiseSigma = 25.5;
input = imnoise(label,'gaussian',0,(imageNoiseSigma/255)^2);
if mod(w,2)==1
input = cat(1,input, input(end,:,:)) ;
end
if mod(h,2)==1
input = cat(2,input, input(:,end,:)) ;
end
% tic;
if useGPU
input = gpuArray(input);
end
% set noise level map
sigmas = inputNoiseSigma/255; % see "vl_simplenn.m".
% perform denoising
res = vl_simplenn(net,input,[],[],'conserveMemory',true,'mode','test'); % matconvnet default
% res = vl_ffdnet_concise(net, input); % concise version of vl_simplenn for testing FFDNet
% res = vl_ffdnet_matlab(net, input); % use this if you did not install matconvnet; very slow
% output = input -res(end).x; % for 'model_color.mat'
output = res(end).x;
if mod(w,2)==1
output = output(1:end-1,:,:);
input = input(1:end-1,:,:);
end
if mod(h,2)==1
output = output(:,1:end-1,:);
input = input(:,1:end-1,:);
end
if useGPU
output = gather(output);
input = gather(input);
end
%toc;
% calculate PSNR, SSIM and save results
[PSNRCur, SSIMCur] = Cal_PSNRSSIM(im2uint8(label),im2uint8(output),0,0);
if showResult
imshow(cat(2,im2uint8(input),im2uint8(label),im2uint8(output)));
title([filePaths(i).name,' ',num2str(PSNRCur,'%2.2f'),'dB',' ',num2str(SSIMCur,'%2.4f')])
%imwrite(im2uint8(output), fullfile(folderResultCur, [nameCur, '_' num2str(imageNoiseSigma(1),'%02d'),'_' num2str(inputNoiseSigma(1),'%02d'),'_PSNR_',num2str(PSNRCur*100,'%4.0f'), extCur] ));
drawnow;
pause()
end
disp([filePaths(i).name,' ',num2str(PSNRCur,'%2.2f'),'dB',' ',num2str(SSIMCur,'%2.4f')])
PSNRs(i) = PSNRCur;
SSIMs(i) = SSIMCur;
end
end
disp([mean(PSNRs),mean(SSIMs)]);