Init

utilities/Cal_PSNRSSIM.m

@@ -0,0 +1,221 @@
+function [psnr_cur, ssim_cur] = Cal_PSNRSSIM(A,B,row,col)
+
+
+[n,m,ch]=size(B);
+A = A(row+1:n-row,col+1:m-col,:);
+B = B(row+1:n-row,col+1:m-col,:);
+A=double(A); % Ground-truth
+B=double(B); %
+
+e=A(:)-B(:);
+mse=mean(e.^2);
+psnr_cur=10*log10(255^2/mse);
+
+if ch==1
+    [ssim_cur, ~] = ssim_index(A, B);
+else
+    ssim_cur = (ssim_index(A(:,:,1), B(:,:,1)) + ssim_index(A(:,:,2), B(:,:,2)) + ssim_index(A(:,:,3), B(:,:,3)))/3;
+end
+
+
+function [mssim, ssim_map] = ssim_index(img1, img2, K, window, L)
+
+%========================================================================
+%SSIM Index, Version 1.0
+%Copyright(c) 2003 Zhou Wang
+%All Rights Reserved.
+%
+%The author is with Howard Hughes Medical Institute, and Laboratory
+%for Computational Vision at Center for Neural Science and Courant
+%Institute of Mathematical Sciences, New York University.
+%
+%----------------------------------------------------------------------
+%Permission to use, copy, or modify this software and its documentation
+%for educational and research purposes only and without fee is hereby
+%granted, provided that this copyright notice and the original authors'
+%names appear on all copies and supporting documentation. This program
+%shall not be used, rewritten, or adapted as the basis of a commercial
+%software or hardware product without first obtaining permission of the
+%authors. The authors make no representations about the suitability of
+%this software for any purpose. It is provided "as is" without express
+%or implied warranty.
+%----------------------------------------------------------------------
+%
+%This is an implementation of the algorithm for calculating the
+%Structural SIMilarity (SSIM) index between two images. Please refer
+%to the following paper:
+%
+%Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image
+%quality assessment: From error measurement to structural similarity"
+%IEEE Transactios on Image Processing, vol. 13, no. 1, Jan. 2004.
+%
+%Kindly report any suggestions or corrections to zhouwang@ieee.org
+%
+%----------------------------------------------------------------------
+%
+%Input : (1) img1: the first image being compared
+%        (2) img2: the second image being compared
+%        (3) K: constants in the SSIM index formula (see the above
+%            reference). defualt value: K = [0.01 0.03]
+%        (4) window: local window for statistics (see the above
+%            reference). default widnow is Gaussian given by
+%            window = fspecial('gaussian', 11, 1.5);
+%        (5) L: dynamic range of the images. default: L = 255
+%
+%Output: (1) mssim: the mean SSIM index value between 2 images.
+%            If one of the images being compared is regarded as
+%            perfect quality, then mssim can be considered as the
+%            quality measure of the other image.
+%            If img1 = img2, then mssim = 1.
+%        (2) ssim_map: the SSIM index map of the test image. The map
+%            has a smaller size than the input images. The actual size:
+%            size(img1) - size(window) + 1.
+%
+%Default Usage:
+%   Given 2 test images img1 and img2, whose dynamic range is 0-255
+%
+%   [mssim ssim_map] = ssim_index(img1, img2);
+%
+%Advanced Usage:
+%   User defined parameters. For example
+%
+%   K = [0.05 0.05];
+%   window = ones(8);
+%   L = 100;
+%   [mssim ssim_map] = ssim_index(img1, img2, K, window, L);
+%
+%See the results:
+%
+%   mssim                        %Gives the mssim value
+%   imshow(max(0, ssim_map).^4)  %Shows the SSIM index map
+%
+%========================================================================
+
+
+if (nargin < 2 || nargin > 5)
+    ssim_index = -Inf;
+    ssim_map = -Inf;
+    return;
+end
+
+if (size(img1) ~= size(img2))
+    ssim_index = -Inf;
+    ssim_map = -Inf;
+    return;
+end
+
+[M N] = size(img1);
+
+if (nargin == 2)
+    if ((M < 11) || (N < 11))
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return
+    end
+    window = fspecial('gaussian', 11, 1.5);	%
+    K(1) = 0.01;								      % default settings
+    K(2) = 0.03;								      %
+    L = 255;                                  %
+end
+
+if (nargin == 3)
+    if ((M < 11) || (N < 11))
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return
+    end
+    window = fspecial('gaussian', 11, 1.5);
+    L = 255;
+    if (length(K) == 2)
+        if (K(1) < 0 || K(2) < 0)
+            ssim_index = -Inf;
+            ssim_map = -Inf;
+            return;
+        end
+    else
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return;
+    end
+end
+
+if (nargin == 4)
+    [H W] = size(window);
+    if ((H*W) < 4 || (H > M) || (W > N))
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return
+    end
+    L = 255;
+    if (length(K) == 2)
+        if (K(1) < 0 || K(2) < 0)
+            ssim_index = -Inf;
+            ssim_map = -Inf;
+            return;
+        end
+    else
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return;
+    end
+end
+
+if (nargin == 5)
+    [H W] = size(window);
+    if ((H*W) < 4 || (H > M) || (W > N))
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return
+    end
+    if (length(K) == 2)
+        if (K(1) < 0 || K(2) < 0)
+            ssim_index = -Inf;
+            ssim_map = -Inf;
+            return;
+        end
+    else
+        ssim_index = -Inf;
+        ssim_map = -Inf;
+        return;
+    end
+end
+
+C1 = (K(1)*L)^2;
+C2 = (K(2)*L)^2;
+window = window/sum(sum(window));
+img1 = double(img1);
+img2 = double(img2);
+
+mu1   = filter2(window, img1, 'valid');
+mu2   = filter2(window, img2, 'valid');
+mu1_sq = mu1.*mu1;
+mu2_sq = mu2.*mu2;
+mu1_mu2 = mu1.*mu2;
+sigma1_sq = filter2(window, img1.*img1, 'valid') - mu1_sq;
+sigma2_sq = filter2(window, img2.*img2, 'valid') - mu2_sq;
+sigma12 = filter2(window, img1.*img2, 'valid') - mu1_mu2;
+
+if (C1 > 0 & C2 > 0)
+    ssim_map = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))./((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2));
+else
+    numerator1 = 2*mu1_mu2 + C1;
+    numerator2 = 2*sigma12 + C2;
+    denominator1 = mu1_sq + mu2_sq + C1;
+    denominator2 = sigma1_sq + sigma2_sq + C2;
+    ssim_map = ones(size(mu1));
+    index = (denominator1.*denominator2 > 0);
+    ssim_map(index) = (numerator1(index).*numerator2(index))./(denominator1(index).*denominator2(index));
+    index = (denominator1 ~= 0) & (denominator2 == 0);
+    ssim_map(index) = numerator1(index)./denominator1(index);
+end
+
+mssim = mean2(ssim_map);
+
+return
+
+
+
+
+
+
+

utilities/center_crop.m

@@ -0,0 +1,9 @@
+function [iout] = center_crop(im,a,b)
+
+[w,h,~] = size(im);
+c1 = w-a-(w-a)/2;
+c2 = h-b-(h-b)/2;
+iout = im(c1+1:c1+a,c2+1:c2+b,:);
+
+end
+

utilities/data_augmentation.m

@@ -0,0 +1,60 @@
+function image = data_augmentation(image, mode)
+
+if mode == 1
+    return;
+end
+
+if mode == 2 % flipped
+    image = flipud(image);
+    return;
+end
+
+if mode == 3 % rotation 90
+    image = rot90(image,1);
+    return;
+end
+
+if mode == 4 % rotation 90 & flipped
+    image = rot90(image,1);
+    image = flipud(image);
+    return;
+end
+
+if mode == 5 % rotation 180
+    image = rot90(image,2);
+    return;
+end
+
+if mode == 6 % rotation 180 & flipped
+    image = rot90(image,2);
+    image = flipud(image);
+    return;
+end
+
+if mode == 7 % rotation 270
+    image = rot90(image,3);
+    return;
+end
+
+if mode == 8 % rotation 270 & flipped
+    image = rot90(image,3);
+    image = flipud(image);
+    return;
+end
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

utilities/im_crop.m

@@ -0,0 +1,12 @@
+function [im] = im_crop(im,w,h)
+
+
+if mod(w,2)==1
+    im = im(1:end-1,:,:);
+end
+if mod(h,2)==1
+    im = im(:,1:end-1,:);
+end
+
+end
+

utilities/im_pad.m

@@ -0,0 +1,13 @@
+function [im] = im_pad(im)
+
+[w,h,~] = size(im);
+
+if mod(w,2)==1
+    im = cat(1,im, im(end,:,:)) ;
+end
+if mod(h,2)==1
+    im = cat(2,im, im(:,end,:)) ;
+end
+
+end
+

utilities/modcrop.m

@@ -0,0 +1,12 @@
+function imgs = modcrop(imgs, modulo)
+if size(imgs,3)==1
+    sz = size(imgs);
+    sz = sz - mod(sz, modulo);
+    imgs = imgs(1:sz(1), 1:sz(2));
+else
+    tmpsz = size(imgs);
+    sz = tmpsz(1:2);
+    sz = sz - mod(sz, modulo);
+    imgs = imgs(1:sz(1), 1:sz(2),:);
+end
+

utilities/shave.m

@@ -0,0 +1,3 @@
+function I = shave(I, border)
+I = I(1+border(1):end-border(1), ...
+      1+border(2):end-border(2), :, :);

utilities/vl_simplenn_mergebnorm.m

@@ -0,0 +1,36 @@
+function [net1] = vl_simplenn_mergebnorm(net)
+
+%% merge bnorm parameters into adjacent Conv layer
+
+for i = 1:numel(net.layers)
+    if strcmp(net.layers{i}.type, 'conv')
+        net.layers{i}.weightDecay(2) = 1;
+    end
+end
+
+for i = 1:numel(net.layers)
+    if strcmp(net.layers{i}.type, 'bnorm')
+        ws = net.layers{i}.weights{1};
+        bs = net.layers{i}.weights{2};
+        mu_sigmas = net.layers{i}.weights{3};
+        for j = 1:numel(ws)
+            net.layers{i-1}.weights{1}(:,:,:,j) =single(double(net.layers{i-1}.weights{1}(:,:,:,j))*double(ws(j))/(double(mu_sigmas(j,2))));
+            net.layers{i-1}.weights{2}(j) =single(double(bs(j)) - double(ws(j))*double(mu_sigmas(j,1))/(double(mu_sigmas(j,2))));
+        end
+        net.layers{i-1}.learningRate(2) = 1;
+    end
+end
+
+net1 = net;
+net1.layers = {};
+net1 = rmfield(net1,'meta');
+for i = 1:numel(net.layers)
+    if ~strcmp(net.layers{i}.type, 'bnorm')
+        net1.layers{end+1} = net.layers{i};
+    end
+end
+
+net1.layers = net1.layers(1:end-1);
+
+
+end