【图像融合】基于形态学分析结合稀疏表征实现图像融合附matlab代码

1 简介

In this letter, a sparse representation (SR) model named convolutional sparsity based morphological component analysis (CS-MCA) is introduced for pixel-level medical image fusion. Unlike the standard SR model, which is based on single image component and​

2 部分代码

clear allclose allclc​addpath(genpath('SparseCode'));addpath(genpath('Util'));​load('dictionary/Dc_8_8_32.mat')load('dictionary/Dt_8_8_32.mat')​img1=imread('sourceimages/source1.tif');img2=imread('sourceimages/source2.tif');​​imgf=CSMCA_Fusion(img1,img2,Dc,Dt);figure,subplot(131);imshow(img1);title('图1')subplot(132);imshow(img2);title('图2')subplot(133);imshow(imgf);title('融合图')imwrite(imgf,'Results/fused_csmca.tif');​​​​​

3 仿真结果

4 参考文献

[1]Liu, Yu, Chen, et al. Medical Image Fusion via Convolutional Sparsity Based Morphological Component Analysis[J]. IEEE Signal Processing Letters, 2019.

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