1 简介
随着工业技术的发展，数字化图像获取设备越来越多，数字图像的应用环境也越来越广泛。数字图像在医学、航天、军事、气象、传媒等诸多领域都起着举足轻重的作用。由于图像在获取和传播过程中不可避免地会受到噪声污染，因此，图像去噪是图像处理的一个关键环节。
去噪效果也直接影响到图像分析、识别等后续任务。图像去噪目的是在尽可能保持图像有用细节的前提下，通过去除噪声，从而获得更为真实的图像。当今，无论在学术界还是工业界，图像去噪都是一个研究热点。
2 部分代码
%%denoising demo using Non-Local Euclidean Medians (NLEM)%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% img : clean grayscale image% h : width of Gaussian% P : half-size of patch % S : half-search window %% Author: Kunal N. Chaudhury% Date: June 10, 2012%% Reference: % K. N. Chaudhury, A. Singer, "Non-Local Euclidean Medians", IEEE Signal% Processing Letters, vol. 19, no. 11, 2012. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%clc; clear all; close all force;% clean imageimg = double(imread('ckb.jpg'));[m, n] = size(img);% add noisesigma = 70;imgNoisy = img + sigma * randn(m,n);% NLEM parametersS = 10;P = 3;h = 10 * sigma;% call NELM (or NLEM_kNN)imgDenoised = NLEM(imgNoisy, h, P, S);% show resultspeak = max(max(img));PSNR0 = 10 * log10(m * n * peak^2 / sum(sum((imgNoisy - img).^2)) );PSNR1 = 10 * log10(m * n * peak^2 / sum(sum((imgDenoised - img).^2)) );figure();colormap gray,subplot(1,3,1), imagesc(img), title('Original', 'FontSize', 10), axis('image', 'off');subplot(1,3,2), imagesc(imgNoisy), title([ 'Noisy, ', num2str(PSNR0, '%.2f'), 'dB'] , 'FontSize', 10), axis('image', 'off');subplot(1,3,3), imagesc(imgDenoised), title([ 'NLEM filtered, ', num2str(PSNR1, '%.2f'), 'dB'] , 'FontSize', 10),axis('image', 'off');
3 仿真结果
4 参考文献
[1]孙忠贵. 非局部均值滤波器研究及应用[D]. 南京航空航天大学.
