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Content introduction
In today’s digital image processing field, image fusion is an important research direction. The goal of image fusion is to fuse images from different sensors or different modalities into an image with more information. Among them, the fusion of visible light and infrared images is a popular research direction because it can provide more comprehensive and accurate information, which is helpful for various application fields, such as military, security and medicine.
In visible and infrared image fusion, saliency detection is a key step. Saliency detection refers to extracting salient areas from images, that is, areas that attract human attention. These salient areas usually contain important information in the image and therefore play an important role in image fusion. In traditional image fusion methods, global saliency detection methods are usually used, but these methods are not ideal for detecting salient areas at different scales.
To solve this problem, researchers proposed a visible and infrared image fusion method based on two-scale saliency detection. This method exploits the salient regions at multiple scales present in the image and fuses them into the final fused image. Specifically, visible and infrared images are first pre-processed, including steps such as denoising and enhancement, to improve the effect of subsequent processing. Then, saliency detection at two scales is performed on the two images respectively to obtain salient area maps at different scales. Next, the salient area maps on different scales are fused through a certain weighting strategy to obtain the final salient area map. Finally, the salient area map is fused with the original image to obtain the final fused image.
This visible and infrared image fusion method based on two-scale saliency detection has many advantages. First, it can make full use of the salient information at multiple scales in the image to improve the quality and accuracy of the fused image. Secondly, it can adapt to the needs of different scenarios and goals, and has good versatility and applicability. In addition, this method has high real-time performance and computational efficiency, and can be used in practical applications.
However, there are still some challenges and problems in visible and infrared image fusion methods based on two-scale saliency detection. First, the accuracy and stability of saliency detection still need to be further improved to improve the quality of fused images. Secondly, the selection and optimization of weighting strategies is also a key issue that requires more research and exploration. In addition, this method may have certain limitations when dealing with complex scenes and targets, and needs further improvement and perfection.
In short, visible and infrared image fusion based on two-scale saliency detection is a research direction with broad application prospects. By fully utilizing the salient information at multiple scales in the image, this method can improve the quality and accuracy of the fused image and adapt to the needs of different scenarios and targets. However, this method still faces some challenges and problems and requires further research and improvement. It is believed that with the continuous development and advancement of technology, the visible and infrared image fusion method based on two-scale saliency detection will play a greater role in practical applications.
Part of the code
function [Xc, Xt]= CSMCA(s, iters, Dc, Dt)
[h,w]=size(s);
xc=zeros(h,w);
xt=zeros(h,w);
for i=1:2*iters
residue=s-xt-xc;
kk=mod(i,2);
iter=round(i/2);
% update cartoon component
ifkk==1
xc=xc + residue;
D=Dc;
lambda_c=max(0.6-0.1*iter,0.005); %For texture 1
opt_c = [];
opt_c.Verbose = 10;
opt_c.MaxMainIter = 30;
opt_c.rho = 50*lambda_c + 1;
opt_c.RelStopTol = 1e-3;
opt_c.AuxVarObj = 0;
opt_c.HighMemSolve = 1;
[Xc, optinf] = cbpdn(D, xc, lambda_c, opt_c);
DX = ifft2(sum(bsxfun(@times, fft2(D, size(Xc,1), size(Xc,2)), fft2(Xc)),3), ...
'symmetric');
xc=DX;
end
% update texture component
ifkk==0
xt=xt + residue;
D=Dt;
lambda_t=max(0.6-0.1*iter,0.005);
opt_t = [];
opt_t.Verbose = 1;
opt_t.MaxMainIter = 30;
opt_t.rho = 10*0.1;
opt_t.RelStopTol = 1e-3;
opt_t.AuxVarObj = 0;
opt_t.HighMemSolve = 1;
[Xt, optinf] = cbpdn(D, xt, lambda_t, opt_t);
DX = ifft2(sum(bsxfun(@times, fft2(D, size(Xt,1), size(Xt,2)), fft2(Xt)),3), ...
'symmetric');
xt=DX;
end
if mod(i,2)==1
fprintf('iteration %d \
',iter)
end
end
Run results
References
