1 内容介绍
在图像融合文献中，多尺度变换（MST）和稀疏表示（SR）是最常用的两个
广泛使用的信号/图像表示理论。本文提出了一个通用的图像融合框架
通过结合 MST 和 SR 同时克服基于 MST 和 SR 的融合方法的固有缺陷。在我们的融合框架中，首先对每个预注册的源图像以获得它们的低通和高通系数。然后，合并低通带使用基于 SR 的融合方法，而高通带使用 coef- 的绝对值进行融合ficients 作为活动水平的测量。最后通过逆MST得到融合图像
关于合并系数。所提出的融合框架相对于单个 MST 或首先从理论的角度详细展示了基于 SR 的方法，然后通过实验通过多焦点、可见红外和医学图像融合验证。特别是六大流行的多尺度变换，包括拉普拉斯金字塔 (LP)、低通金字塔比 (RP)、离散小波变换(DWT)、双树复小波变换 (DTCWT)、曲波变换 (CVT) 和非下采样Contourlet 变换 (NSCT)，测试了从 1 到 4 的不同分解级别在我们的实验中。通过对融合结果进行主观和客观的比较，我们给出了在所提出的框架下对每一类图像融合的最佳融合方法。效果还研究了滑动窗口的步长。此外，实验结果表明所提出的融合框架可以获得最先进的性能，特别是对于融合多模态图像。
2 仿真代码
clear all;close all;clc;addpath(genpath('sparsefusion'));addpath(genpath('dtcwt_toolbox'));addpath(genpath('fdct_wrapping_matlab'));addpath(genpath('nsct_toolbox'));load('sparsefusion/Dictionary/D_100000_256_8.mat');[imagename1 imagepath1]=uigetfile('source_images\*.jpg;*.bmp;*.png;*.tif;*.tiff;*.pgm;*.gif','Please choose the first input image');image_input1=imread(strcat(imagepath1,imagename1)); [imagename2 imagepath2]=uigetfile('source_images\*.jpg;*.bmp;*.png;*.tif;*.tiff;*.pgm;*.gif','Please choose the second input image');image_input2=imread(strcat(imagepath2,imagename2)); figure;subplot(121);imshow(image_input1);title('图1');subplot(122);imshow(image_input2);title('图2')if size(image_input1)~=size(image_input2) error('two images are not the same size.');endimg1=double(image_input1);img2=double(image_input2);overlap = 6; epsilon=0.1;level=4;% To make a comparison, please use % LP-SR for medical image fusion, % DTCWT-SR for visible-infrared image fusion,% NSCT-SR for multi-focus image fusion.tic;if size(img1,3)==1 %for gray images imgf = lp_sr_fuse(img1,img2,level,3,3,D,overlap,epsilon); %LP-SR imgf1 = rp_sr_fuse(img1,img2,level,3,3,D,overlap,epsilon); %RP-SR imgf2 = dwt_sr_fuse(img1,img2,level,D,overlap,epsilon); %DWT-SR imgf3 = dtcwt_sr_fuse(img1,img2,level,D,overlap,epsilon); %DTCWT-SR imgf4 = curvelet_sr_fuse(img1,img2,level+1,D,overlap,epsilon); %CVT-SR imgf5 = nsct_sr_fuse(img1,img2,[2,3,3,4],D,overlap,epsilon); %NSCT-SRelse %for color images imgf=zeros(size(img1)); imgf1=zeros(size(img1)); imgf2=zeros(size(img1)); imgf3=zeros(size(img1)); imgf4=zeros(size(img1)); imgf5=zeros(size(img1)); for i=1:3 imgf(:,:,i) = lp_sr_fuse(img1(:,:,i),img2(:,:,i),level,3,3,D,overlap,epsilon); %LP-SR imgf1(:,:,i) = rp_sr_fuse(img1(:,:,i),img2(:,:,i),level,3,3,D,overlap,epsilon); %RP-SR imgf2(:,:,i) = dwt_sr_fuse(img1(:,:,i),img2(:,:,i),level,D,overlap,epsilon); %DWT-SR imgf3(:,:,i) = dtcwt_sr_fuse(img1(:,:,i),img2(:,:,i),level,D,overlap,epsilon); %DTCWT-SR imgf4(:,:,i) = curvelet_sr_fuse(img1(:,:,i),img2(:,:,i),level+1,D,overlap,epsilon); %CVT-SR imgf5(:,:,i) = nsct_sr_fuse(img1(:,:,i),img2(:,:,i),[2,3,3,4],D,overlap,epsilon); %NSCT-SR endendtoc;figure;;subplot(231);imshow(uint8(imgf));title('LP-SR');subplot(232);imshow(uint8(imgf1));title('RP-SR')subplot(233);imshow(uint8(imgf2));title('DWT-SR')subplot(234);imshow(uint8(imgf3));title('DTCWT-SR')subplot(235);imshow(uint8(imgf4));title('CVT-SR')subplot(236);imshow(uint8(imgf5));title('NSCT-SR')imwrite(uint8(imgf),'Results/fused_mstsr.tif');3 运行结果
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