✅作者简介：热爱科研的Matlab仿真开发者，修心和技术同步精进，matlab项目合作可私信。
🍎个人主页：Matlab科研工作室
🍊个人信条：格物致知。
更多Matlab仿真内容点击👇
⛄ 内容介绍
In this paper the Cross Bilateral Filter is used to fuse source images with the help of Mahalanobis distance measure. The proposed image fusion algorithm directly fuses two source images of a same scene using weighted average. The proposed method differs from other weighted average methods in terms of weight computation and the domain of weighted average. Here, the weights are computed by measuring the strength of details in a detail image obtained by subtracting CBF output from original image. The weights thus computed are multiplied directly with the original source images followed by weight normalization. This paper compares the few similar image fusion algorithms by considering the performance evaluation metrics like Entropy, Standard Deviation, and PSNR.Keywords: Cross Bilateral Filter, Mahalanobis distance, Entropy, Standard Deviation, PSNR.
⛄ 部分代码
function xcovar=covarf(x,cov_wsize)
%%% covarf: computes covariance of a signal.
%%% covarf(X), if X is a vector, returns the variance.
%%% For matrices, where each row is an observation, and each column a variable,
%%% covarf(X) is the covariance matrix.
%%% diag(covarf(X)) is a vector of variances for each column.
%%% sqrt(diag(covarf(X))) is a vector of standard deviations.
%%% wsize should be odd.
%%% Author : B. K. SHREYAMSHA KUMAR
%%% Created on 15-02-2012.
%%% Updated on 15-02-2012.
tr=x-repmat(mean(x),cov_wsize,1);
xcovar=tr'*tr/(cov_wsize-1);
⛄ 运行结果
⛄ 参考文献
[1] Kishore K , Nagaraju N , Kumar A . An Adaptive Multi-Focus Medical Image Fusion using Cross Bilateral Filter Based on Mahalanobis Distance Measure.
