【图像去噪】基于双立方插值和稀疏表示实现图像去噪matlab源码

% ========================================================================= % Simple demo codes for image super-resolution via sparse representation % % Reference % ========================================================================= clear all; clc; % read test image im_l = imread(’Data/Testing/input.bmp’); % set parameters lambda = 0.2; % sparsity regularization overlap = 4; % the more overlap the better (patch size 5x5) up_scale = 2; % scaling factor, depending on the trained dictionary maxIter = 20; % if 0, do not use backprojection % load dictionary load(’Dictionary/D_1024_0.15_5.mat’); % change color space, work on illuminance only im_l_ycbcr = rgb2ycbcr(im_l); im_l_y = im_l_ycbcr(:, :, 1); im_l_cb = im_l_ycbcr(:, :, 2); im_l_cr = im_l_ycbcr(:, :, 3); % image super-resolution based on sparse representation [im_h_y] = ScSR(im_l_y, 2, Dh, Dl, lambda, overlap); [im_h_y] = backprojection(im_h_y, im_l_y, maxIter); % upscale the chrominance simply by "bicubic" [nrow, ncol] = size(im_h_y); im_h_cb = imresize(im_l_cb, [nrow, ncol], ’bicubic’); im_h_cr = imresize(im_l_cr, [nrow, ncol], ’bicubic’); im_h_ycbcr = zeros([nrow, ncol, 3]); im_h_ycbcr(:, :, 1) = im_h_y; im_h_ycbcr(:, :, 2) = im_h_cb; im_h_ycbcr(:, :, 3) = im_h_cr; im_h = ycbcr2rgb(uint8(im_h_ycbcr)); % bicubic interpolation for reference im_b = imresize(im_l, [nrow, ncol], ’bicubic’); % read ground truth image im = imread(’Data/Testing/gnd.bmp’); % compute PSNR for the illuminance channel bb_rmse = compute_rmse(im, im_b); sp_rmse = compute_rmse(im, im_h); bb_psnr = 20*log10(255/bb_rmse); sp_psnr = 20*log10(255/sp_rmse); % show the images figure, subplot(131),imshow(im_l);title(’原图’) subplot(132),imshow(im_h); title([’PSNR for 稀疏表示’,num2str( sp_psnr)]); subplot(133), imshow(im_b); title([’PSNR for 双立方插值’,num2str(bb_psnr)]);