图像相似度计算方法集_loseyel_颜色集_相似度计算maltab_相似度_颜色相似度

%%%%suggestion of usage function [mssim, ssim_map] = ssim(img1, img2, K, window, L) % ======================================================================== % SSIM Index with automatic downsampling, Version 1.0 % Copyright(c) 2009 Zhou Wang % All Rights Reserved. % % ---------------------------------------------------------------------- % 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 and the website with suggested usage % % Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image % quality assessment: From error visibility to structural similarity," % IEEE Transactios on Image Processing, vol. 13, no. 4, pp. 600-612, % Apr. 2004. % % http://www.ece.uwaterloo.ca/~z70wang/research/ssim/ % % Note: This program is different from ssim_index.m, where no automatic % downsampling is performed. (downsampling was done in the above paper % and was described as suggested usage in the above website.) % % 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 % depends on the window size and the downsampling factor. % %Basic Usage: % Given 2 test images img1 and img2, whose dynamic range is 0-255 % % [mssim, ssim_map] = ssim(img1, img2); % %Advanced Usage: % User defined parameters. For example % % K = [0.05 0.05]; % window = ones(8); % L = 100; % [mssim, ssim_map] = ssim(img1, img2, K, window, L); % %Visualize the results: % % mssim %Gives the mssim value % imshow(max(0, ssim_map).^4) %Shows the SSIM index map %======================================================================== if (nargin < 2 || nargin > 5) %参数个数小于2个或者大于5个,则退出 mssim = -Inf; ssim_map = -Inf; return; end if (size(img1) ~= size(img2)) %对比的两幅图大小要一致，否则退出 mssim = -Inf; ssim_map = -Inf; return; end [M, N] = size(img1); %将图1的大小赋值给M N if (nargin == 2) %参数为2时 if ((M < 11) || (N < 11)) %图像长宽都不能小于11，否则退出 mssim = -Inf; ssim_map = -Inf; return end window = fspecial('gaussian', 11, 1.5); %建立预定义的滤波算子。 %为高斯低通滤波，有两个参数，hsize表示模板尺寸，默认值为[3 3]，sigma为滤波器的标准值，单位为像素，默认值为0.5. K(1) = 0.01; % default settings K(2) = 0.03; %K L参数设置为最佳默认值 L = 255; %设置L的默认值 end if (nargin == 3) %参数为3个时，第3个参数为K if ((M < 11) || (N < 11)) mssim = -Inf; ssim_map = -Inf; return end window = fspecial('gaussian', 11, 1.5); %获取滤波算子，类型为gaussian，11为窗口尺寸，1.5为标准差 L = 255; if (length(K) == 2) %参数K为2个元素的数组，且都大于0 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) %参数3为K，参数4为窗口大小 [H, W] = size(window); %window参数类似ones(8) if ((H*W) < 4 || (H > M) || (W > N)) %窗口大小要求大于4或者长宽不小于图像的长宽 mssim = -Inf; ssim_map = -Inf; return end L = 255; if (length(K) == 2) %判断K数组的大小 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 == 5) %当后3个参数都设置时，其中L参数执行传入的参数 [H, W] = size(window); if ((H*W) < 4 || (H > M) || (W > N)) mssim = -Inf; ssim_map = -Inf; return end 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 img1 = double(img1); img2 = double(img2); % automatic downsampling f = max(1,round(min(M,N)/256)); %先选择行列两者中的最小值取整，再选择其中的最大值 %downsampling by f %use a simple low-pass filter if(f>1) lpf = ones(f,f); %初始化一个单位矩阵，用于归一化 lpf = lpf/sum(lpf(:)); %归一化，除以单位矩阵的个数 img1 = imfilter(img1,lpf,'symmetric','same'); %使用滤波函数对img1进行处理，lpf是归一化的滤波模板， %边界使用symmetric镜像反射填充边界 img2 = imfilter(img2,lpf,'symmetric','same'); %%% 均值滤波 img1 = img1(1:f:end,1:f:end); %向下隔点取样 img2 = img2(1:f:end,1:f:end); end C1 = (K(1)*L)^2; %求取论文中C1的值 C2 = (K(2)*L)^2; %求取论文中C2的值 window = window/sum(sum(window)); %滤波器归一化操作。缺省的sum(x)就是竖向相加，求每列的和，结果是行向量 mu1 = filter2(window, img1, 'valid'); %使用设定好的高斯低通滤波器window对img1进行滤波，结果保存在mu1中 %mu1相当于论文中的Ux,即图像img1的均值 mu2 = filter2(window, img2, 'valid'); %mu2相当于论文中的Uy，即图像img2的均值，点乘模板相加，因为window归一化了，所以是均值 mu1_sq = mu1.*mu1; %矩阵运算，相当于img1均值的矩阵乘法平方 mu2_sq = mu2.*mu2; mu1_mu2 = mu1.*mu2; %img1和img2均值的矩阵乘法平方 sigma1_sq = filter2(window, img1.*img1, 'valid') - mu1_sq; %协方差期望公式：sigma_x=E（X^2）-（EX）^2 sigma2_sq = filter2(window, img2.*img2, 'valid') - mu2_sq; %协方差期望公式：sigma_y=E（Y^2）-（EY）^2 sigma12 = filter2(window, img1.*img2, 'valid') - mu1_mu2; %协方差期望公式：sigma_xy=E(XY）-（EX）*（EY） 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