[image denoising] image denoising based on bicube interpolation and sparse representation matlab source code

1 Content introduction

This paper solves the problem of generating super-resolution from a single low resolution input image (SR) The problem with images . We solve this problem from the perspective of compressed sensing . The low resolution image is regarded as the down sampled version of the high resolution image , Suppose that the patch has a sparse representation relative to the over complete dictionary of the prototype signal atom . The principle of compressed sensing ensures that under mild conditions , Sparse representation can be correctly recovered from the down sampled signal . We will prove the effectiveness of sparsity as a priori for the specification of otherwise ill posed super-resolution problems . We further show that , A small group of original patches randomly selected from training images with similar statistical properties to the input image can usually be used as a good dictionary , Because the calculated representation is sparse , The restored high-resolution image is competitive and even better than others SR Method generated image .

2 Simulation code

% =========================================================================% Simple demo codes for image super-resolution via sparse representation%% Reference​​% =========================================================================​clear all; clc;​% read test imageim_l = imread('Data/Testing/input.bmp');​% set parameterslambda = 0.2;                   % sparsity regularizationoverlap = 4;                    % the more overlap the better (patch size 5x5)up_scale = 2;                   % scaling factor, depending on the trained dictionarymaxIter = 20;                   % if 0, do not use backprojection​% load dictionaryload('Dictionary/D_1024_0.15_5.mat');​% change color space, work on illuminance onlyim_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 referenceim_b = imresize(im_l, [nrow, ncol], 'bicubic');​% read ground truth imageim = imread('Data/Testing/gnd.bmp');​% compute PSNR for the illuminance channelbb_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 imagesfigure, subplot(131),imshow(im_l);title(' Original picture ')subplot(132),imshow(im_h);title(['PSNR for  Sparse representation ',num2str( sp_psnr)]);subplot(133), imshow(im_b);title(['PSNR for  Bicubic interpolation ',num2str(bb_psnr)]);​​

3 Running results

4 reference

[1] Kingship , Publicize , Li Yanfeng , etc. . An image denoising algorithm based on sparse representation [J]. Industrial instruments and automation devices , 2013.

[2] Liu Meijuan . be based on MATLAB Research on image denoising based on [C]// Challenges and opportunities :2010 Colleges and universities GIS Forum . 0.

[3] Guo Xiaofeng , Zhao Zheng Chen , Liushengqing , etc. . An image denoising method and system based on image sparse representation :, CN109727219A[P]. 2019.

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