【图像融合】基于交叉双边滤波器的图像融合算法研究附matlab代码

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智能优化算法  神经网络预测 雷达通信  无线传感器

信号处理 图像处理 路径规划 元胞自动机 无人机

⛄ 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. 

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