【圖像去噪】基于雙立方插值和稀疏表示實(shí)現(xiàn)圖像去噪matlab源碼
【圖像去噪】基于雙立方插值和稀疏表示實(shí)現(xiàn)圖像去噪matlab源碼
TT_Matlab
博主簡(jiǎn)介：擅長(zhǎng)智能優(yōu)化算法、神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)、信號(hào)處理、元胞自動(dòng)機(jī)、圖像處理、路徑規(guī)劃、無(wú)人機(jī)等多種領(lǐng)域的Matlab仿真，完整matlab代碼或者程序定制加qq1575304183。
1 內(nèi)容介紹
本文解決了從單個(gè)低分辨率輸入圖像生成超分辨率 (SR) 圖像的問(wèn)題。我們從壓縮感知的角度來(lái)解決這個(gè)問(wèn)題。低分辨率圖像被視為高分辨率圖像的下采樣版本，假設(shè)其補(bǔ)丁相對(duì)于原型信號(hào)原子的過(guò)完備字典具有稀疏表示。壓縮感知的原理保證了在溫和的條件下，稀疏表示可以正確地從下采樣信號(hào)中恢復(fù)出來(lái)。我們將證明稀疏性的有效性作為規(guī)范否則不適定的超分辨率問(wèn)題的先驗(yàn)。我們進(jìn)一步表明，從與輸入圖像具有相似統(tǒng)計(jì)性質(zhì)的訓(xùn)練圖像中隨機(jī)選擇的一小組原始補(bǔ)丁通?？梢宰鳛橐粋€(gè)好的字典，因?yàn)橛?jì)算的表示是稀疏的，而恢復(fù)的高分辨率圖像具有競(jìng)爭(zhēng)力甚至質(zhì)量?jī)?yōu)于其他 SR 方法生成的圖像。
2 仿真代碼
% =========================================================================
% 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(’原圖’)
subplot(132),imshow(im_h);
title([’PSNR for 稀疏表示’,num2str( sp_psnr)]);
subplot(133), imshow(im_b);
title([’PSNR for 雙立方插值’,num2str(bb_psnr)]);
3 運(yùn)行結(jié)果
4 參考文獻(xiàn)
[1]王國(guó)權(quán), 張揚(yáng), 李彥鋒,等. 一種基于稀疏表示的圖像去噪算法[J]. 工業(yè)儀表與自動(dòng)化裝置, 2013.
[2]劉美娟. 基于MATLAB的圖像去噪研究[C]// 挑戰(zhàn)與機(jī)遇:2010高校GIS論壇. 0.
[3]郭曉峰, 陳釗正, 劉圣卿,等. 一種基于圖像稀疏表達(dá)的圖像去噪方法及系統(tǒng):, CN109727219A[P]. 2019.