File size: 3,601 Bytes
dcd2bd2 | 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 | % This is the testing demo of FFDNet for denoising real noisy grayscale images.
%
% 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: cskaizhang@gmail.com)
% clear; clc;
format compact;
global sigmas; % input noise level or input noise level map
addpath(fullfile('utilities'));
folderModel = 'models';
folderTest = 'testsets';
folderResult= 'results';
imageSets = {'RNI6'}; % testing datasets
setTestCur = imageSets{1}; % current testing dataset
showResult = 1;
useGPU = 1;
pauseTime = 0;
inputNoiseSigma = 15; % input noise level
% -****************************************************-
% Building.png (inputNoiseSigma = 20); i = 1
% Chupa_Chups.png (inputNoiseSigma = 10); i = 2
% David_Hilbert.png (inputNoiseSigma = 15); i = 3
% Marilyn.png (inputNoiseSigma = 7); i = 4
% Old_Tom_Morris.png (inputNoiseSigma = 15); i = 5
% Vinegar.png (inputNoiseSigma = 20); i = 6
% -****************************************************-
folderResultCur = fullfile(folderResult, [setTestCur,'_',num2str(inputNoiseSigma)]);
if ~isdir(folderResultCur)
mkdir(folderResultCur)
end
load(fullfile('models','FFDNet_gray.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'};
filePaths = [];
for i = 1 : length(ext)
filePaths = cat(1,filePaths, dir(fullfile(folderTest,setTestCur,ext{i})));
end
for i = 1
% read images
disp([filePaths(i).name])
label = imread(fullfile(folderTest,setTestCur,filePaths(i).name));
[w,h,~]=size(label);
if size(label,3)==3
label = rgb2gray(label);
end
[~,nameCur,extCur] = fileparts(filePaths(i).name);
input = im2single(label);
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_gray.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
% convert to CPU
if useGPU
output = gather(output);
input = gather(input);
end
% toc;
if showResult
imshow(cat(2,im2uint8(input),im2uint8(output)));
title([filePaths(i).name])
imwrite(im2uint8(output), fullfile(folderResultCur, [nameCur, '_' num2str(inputNoiseSigma,'%02d'), '.png'] ));
drawnow;
pause(pauseTime)
end
end
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