OpenCV 实现SSIM结构相似性算法

短命女 2022-06-17 02:20 347阅读 0赞

SSIM算法的介绍：  
http://blog.csdn.net/chaipp0607/article/details/70158835

代码做了一下处理：  
（1）设置两组对比试验，将原图进行核为5*5的滤波，与原图比较求得SSIM指数。将原图进行核为10*10的滤波，与原图比较求得SSIM指数。  
（2）将SSIM指数折算为百分制  
（3）采用高斯模糊求得图像的均值

代码参考：  
http://jingyan.baidu.com/article/456c463b67aa310a5931447a.html

#include <opencv2/imgproc/imgproc.hpp>
    #include <opencv2/core/core.hpp>
    #include <opencv2/highgui/highgui.hpp>
    #include <iostream>
    
    using namespace std; 
    using namespace cv;
    
    Scalar getMSSIM(Mat  inputimage1, Mat inputimage2);
    int main()
    {
    	Mat BlurImage1;
    	Mat BlurImage2;
    	Mat SrcImage = imread("1.jpg");
    	blur(SrcImage, BlurImage1, Size(5, 5));
    	blur(SrcImage,BlurImage2,Size(10,10));
    	Scalar SSIM1 = getMSSIM(SrcImage, BlurImage1);
    	Scalar SSIM2 = getMSSIM(SrcImage, BlurImage2);
    	printf("模糊5*5通道1：%f\n", SSIM1.val[0] * 100);
    	printf("模糊5*5通道2：%f\n", SSIM1.val[1] * 100);
    	printf("模糊5*5通道3：%f\n", SSIM1.val[2] * 100);
    	printf("模糊5*5：%f\n", (SSIM1.val[2] + SSIM1.val[1] + SSIM1.val[0])/3 * 100);
    	printf("模糊10*10通道1：%f\n", SSIM2.val[0] * 100);
    	printf("模糊10*10通道2：%f\n", SSIM2.val[1] * 100);
    	printf("模糊10*10通道3：%f\n", SSIM2.val[2] * 100);
    	printf("模糊10*10：%f\n", (SSIM2.val[2] + SSIM2.val[1] + SSIM2.val[0]) / 3 * 100);
    	imshow("原图",SrcImage);
    	imshow("模糊5*5",BlurImage1);
    	imshow("模糊10*10", BlurImage2);
    	waitKey(0);
    	return 0;
    }
    Scalar getMSSIM(Mat  inputimage1, Mat inputimage2)
    {
    	Mat i1 = inputimage1;
    	Mat i2 = inputimage2;
    	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);
    	Mat I1_2 = I1.mul(I1);
    	Mat I1_I2 = I1.mul(I2);
    	Mat 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);
    	t1 = mu1_2 + mu2_2 + C1;
    	t2 = sigma1_2 + sigma2_2 + C2;
    	t1 = t1.mul(t2);
    	Mat ssim_map;
    	divide(t3, t1, ssim_map);
    	Scalar mssim = mean(ssim_map);
    	return mssim;
    }

打印结果：  
模糊5*5通道1：82.523627  
模糊5*5通道2：85.781376  
模糊5*5通道3：85.903646  
模糊5*5：84.736216  
模糊10*10通道1：65.029142  
模糊10*10通道2：69.286267  
模糊10*10通道3：68.664205  
模糊10*10：67.659871

![这里写图片描述][573c59e81d9c09fe9856c5ef229cf8b2.png]  
![这里写图片描述][f2070e863364fa3a66bee77316237319.png]  
![这里写图片描述][991aca4c69c920245fdaef7c42adeae1.png]

[573c59e81d9c09fe9856c5ef229cf8b2.png]: /images/20220617/481ce7a6b28d4b0788af53bf2ff6cb38.png
[f2070e863364fa3a66bee77316237319.png]: /images/20220617/6d513823d1a844fda5ba71c4b0b80648.png
[991aca4c69c920245fdaef7c42adeae1.png]: /images/20220617/498c5e2eec354ad2b221394812d2c847.png