nlpca.rar_NLPCA源码_mri去噪_nlpca_图像去噪_图像去噪 matlab

function [mssim, ssim_map] = ssim_index3d(img1, img2, sw, ind) %======================================================================== %SSIM Index, Version 1.0 %Copyright(c) 2003 Zhou Wang %All Rights Reserved. % %The author was with Howard Hughes Medical Institute, and Laboratory %for Computational Vision at Center for Neural Science and Courant %Institute of Mathematical Sciences, New York University, USA. He is %currently with Department of Electrical and Computer Engineering, %University of Waterloo, Canada. % %---------------------------------------------------------------------- %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. 4, Apr. 2004. % %Kindly report any suggestions or corrections to [email protected] % %---------------------------------------------------------------------- % %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) mssim = -Inf; ssim_map = -Inf; return; end if (size(img1) ~= size(img2)) mssim = -Inf; ssim_map = -Inf; return; end s = size(img1); % sw = [2 2 2]; if (nargin == 2) if ((s(1) < sw(1)) | (s(2) < sw(2)) | (s(3) < sw(3))) mssim = -Inf; ssim_map = -Inf; return end sw = [2 2 2]; ind = find(img1 ~=0); % window = fspecial('gaussian', 11, 1.5); % window = gkernel(1.5,sw); % K(1) = 0.01; % default settings K(2) = 0.03; % L = 255; % end if (nargin == 3) if ((s(1) < sw(1)) | (s(2) < sw(2)) | (s(3) < sw(3))) mssim = -Inf; ssim_map = -Inf; return end % window = fspecial('gaussian', 11, 1.5); window = gkernel(1.5,sw); % ind = find(img1 ~=0); K(1) = 0.01; % default settings K(2) = 0.03; L = 255; if (length(K) == 2) if (K(1) < 0 | K(2) < 0) mssim = -Inf; ssim_map = -Inf; return; end else mssim = -Inf; ssim_map = -Inf; return; end end if (nargin == 4) window = gkernel(1.5,sw); % K(1) = 0.01; % default settings K(2) = 0.03; L = 255; if (length(K) == 2) if (K(1) < 0 | K(2) < 0) mssim = -Inf; ssim_map = -Inf; return; end else mssim = -Inf; ssim_map = -Inf; return; end end C1 = (K(1)*L)^2; C2 = (K(2)*L)^2; window = window/sum(window(:)); img1 = double(img1); img2 = double(img2); mu1 = convn( img1,window, 'same'); mu2 = convn( img2,window, 'same'); mu1_sq = mu1.*mu1; mu2_sq = mu2.*mu2; mu1_mu2 = mu1.*mu2; sigma1_sq = convn( img1.*img1,window, 'same') - mu1_sq; sigma2_sq = convn( img2.*img2,window, 'same') - mu2_sq; sigma12 = convn( img1.*img2,window, 'same') - 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 temp = zeros(size(img1)); temp(ind) = 1; iind = find(temp ==0); ssim_map(iind)=1; mssim = mean(ssim_map(ind)); return function [gaussKernel]=gkernel(sigma,sk) % Pierrick Coupe - [email protected] % Brain Imaging Center, Montreal Neurological Institute. % Mc Gill University % % Copyright (C) 2008 Pierrick Coupe for x = 1:(2*sk(1)+1) for y=1:(2*sk(2)+1) for z=1:(2*sk(3)+1) radiusSquared = (x-(sk(1)+1))^2 + (y-(sk(2)+1))^2 + (z-(sk(3)+1))^2; gaussKernel(x, y, z) = exp(-radiusSquared/(2*sigma^2)); end end end