适用范围：

1、无前景图像的评估，可以评估对这种图像矫正、去除噪声、滤波等等前后两张图像的性能

2、两张特征固定的图像的评估

性能指标为连个：一个是PSNR，基于对图像灰阶的均方差的评估，opencv中double PSNR(InputArray src1, InputArray src2, double R=255.);可以直接调用

第二个是SSIM，对形状结构的评估，代码如下

  Scalar getMSSIM(const Mat& i1, const Mat& i2)
{const double C1 = 6.5025, C2 = 58.5225;int d = CV_32F;Mat I1, I2;i1.convertTo(I1, d);            i2.convertTo(I2, d);Mat I2_2 = I2.mul(I2);        // I2^2Mat I1_2 = I1.mul(I1);        // I1^2Mat I1_I2 = I1.mul(I2);        // I1 * I2Mat mu1, mu2;                  GaussianBlur(I1, mu1, Size(11, 11), 1.5);GaussianBlur(I2, mu2, Size(11, 11), 1.5);Mat mu1_2 = mu1.mul(mu1);Mat mu2_2 = mu2.mul(mu2);Mat mu1_mu2 = mu1.mul(mu2);Mat sigma1_2, sigma2_2, sigma12;GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);sigma1_2 -= mu1_2;GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);sigma2_2 -= mu2_2;GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);sigma12 -= mu1_mu2;Mat t1, t2, t3;t1 = 2 * mu1_mu2 + C1;t2 = 2 * sigma12 + C2;t3 = t1.mul(t2);                 // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))t1 = mu1_2 + mu2_2 + C1;t2 = sigma1_2 + sigma2_2 + C2;t1 = t1.mul(t2);                 // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))Mat ssim_map;divide(t3, t1, ssim_map);        // ssim_map =  t3./t1;Scalar mssim = mean(ssim_map);   // mssim是ssim的平均值return mssim;
}