请写出图像融合评估指标 并用matlab写出计算这些指标的代码

常见的图像融合评估指标包括以下几种：

1. 均方误差（MSE）
2. 峰值信噪比（PSNR）
3. 结构相似性指数（SSIM）
4. 信息熵（Entropy）
5. 互信息（MI）

以下是用 Matlab 实现这些指标的代码：

1. 均方误差（MSE）

function mse = computeMSE(img1, img2)
    diff = double(img1) - double(img2);
    mse = sum(diff(:).^2) / numel(img1);
end

2. 峰值信噪比（PSNR）

function psnr = computePSNR(img1, img2)
    mse = computeMSE(img1, img2);
    psnr = 10 * log10((255^2) / mse);
end

3. 结构相似性指数（SSIM）

function ssimval = computeSSIM(img1, img2)
    k1 = 0.01;
    k2 = 0.03;
    L = 255;
    c1 = (k1 * L)^2;
    c2 = (k2 * L)^2;
    mu1 = mean2(img1);
    mu2 = mean2(img2);
    sigma1 = std2(img1);
    sigma2 = std2(img2);
    sigma12 = cov2(img1, img2);
    ssimval = ((2 * mu1 * mu2 + c1) * (2 * sigma12 + c2)) / ((mu1^2 + mu2^2 + c1) * (sigma1^2 + sigma2^2 + c2));
end

4. 信息熵（Entropy）

function entropyval = computeEntropy(img)
    [counts, ~] = imhist(img);
    p = counts / sum(counts);
    entropyval = -sum(p.*log2(p));
end

5. 互信息（MI）

function mival = computeMI(img1, img2)
    nbins = 256;
    [counts1, ~] = imhist(img1, nbins);
    [counts2, ~] = imhist(img2, nbins);
    jointHist = histcounts2(img1(:), img2(:), nbins);
    jointHist = jointHist / sum(jointHist(:));
    p1 = counts1 / sum(counts1);
    p2 = counts2 / sum(counts2);
    mi = 0;
    for i=1:nbins
        for j=1:nbins
            if jointHist(i,j) > 0
                mi = mi + jointHist(i,j) * log2(jointHist(i,j) / (p1(i) * p2(j)));
            end
        end
    end
    mival = mi;
end