【图像融合】基于像素显着性结合小波变换实现多焦点和多光谱图像融合附matlab代码

1 简介

The energy compaction and multiresolution properties of wavelets have made the image fusion successful in combining important features such as edges and textures from source images without introducing any artifacts for context enhancement and situational awareness. The wavelet transform is visualized as a convolution of wavelet filter coefficients with the image under consideration and is computationally intensive. The advent of lifting-based wavelets has reduced the computations but at the cost of visual quality and performance of the fused image. To retain the visual quality and performance of the fused image with reduced computations, a discrete cosine harmonic wavelet (DCHWT)-based image fusion is proposed. The performance of DCHWT is compared with both convolution and lifting-based image fusion approaches. It is found that the performance of DCHWT is similar to convolution-based wavelets and superior/similar to lifting-based wavelets. Also, the computational complexity (in terms of additions and multiplications) of the proposed method scores over convolution-based wavelets and is competitive to lifting-based wavelets.​

2 部分代码

function [jhist_out]=joint_hist_fn(x1,x2)​%%% joint_hist_fn: Computes the joint histogram of Images x1 and x2.%%% %%% jhist_out=joint_hist_fn(x) %%% Example:%%%   Y = joint_hist_fn(X);  %% takes a pair of images x1 & x2 of equal size and returns the 2d joint histogram.%%%%%% Author : B. K. SHREYAMSHA KUMAR %%% Created on 21-10-2011.%%% Updated on 21-10-2011.​[p,q]=size(x1);jhist_out=zeros(256,256);for ii=1:p   for jj=1:q      jhist_out(x1(ii,jj)+1,x2(ii,jj)+1)=jhist_out(x1(ii,jj)+1,x2(ii,jj)+1)+1;   endend

3 仿真结果

4 参考文献

[1]柏春岚, 刘豪. 基于小波变换的图像融合及其MATLAB实现[J]. 科技广场, 2014(8):3.

[2] Kumar B K S . Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform[J]. Signal, Image and Video Processing, 2013, 7(6):1125-1143.

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