【图像去噪】基于自适应布谷鸟算法优化维纳滤波器实现多光谱图像去噪附matlab代码

1 简介

较传统遥感图像而言,多光谱遥感图像能够获取更多的光谱信息,从而为识别边界和地物创造更加良好的条件,因此具有更高的研究意义和应用价值。随着地理信息系统技术中多光谱图像在空间分辨率上的高速提升,针对多光谱图像的处理及应用也逐渐增加。然而,在多光谱图像的采集过程中不可避免地会引入噪声,影响图像的清晰度,从而给后续的图像处理和分析工作带来困难。​基于自适应布谷鸟算法优化维纳滤波器实现多光谱图像去噪附matlab代码。

2 部分代码

%% 2DACS- Wiener filtering%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Multispectral Satellite Image Denoising via Adaptive Cuckoo Search-Based Wiener Filter%Shilpa Suresh, Shyam Lal, Chen Chen, Turgay Celik%Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 56 , Issue: 8 , Aug. 2018 )%DOI: 10.1109/TGRS.2018.2815281%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%clc;clear all;close all  I = im2double(imread('1.jpg'));r = I(:,:,1);g = I(:,:,2);b = I(:,:,3);sizeorig=size(I); rr = imresize(r,1); gr = imresize(g,1); br = imresize(b,1);Im(:,:,1) = rr;Im(:,:,2) = gr;Im(:,:,3) = br;nvar =  0.01; %variance of noisengrays = imnoise(Im,'gaussian',0,nvar);  %white gaussian noise​%% Denoised imagey=adaptivecucko(Im,ngrays,50);figure;subplot(131);imshow(Im);title('原图')subplot(132);imshow(ngrays);title('加噪图')subplot(133);imshow(y);title('去噪图')​

3 仿真结果

4 参考文献

[1]颜冰等. “一种多光谱图像的去噪和滤波方法及装置.”, CN110298805A. 2019.

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