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main.cpp
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#include <iostream>
#include <fstream>
#include <math.h>
#include <string>
#include <opencv2/opencv.hpp>
#include "split.h"
using namespace std;
using namespace cv;
double getPSNR(const Mat& I1, const Mat& I2)
{
Mat s1;
absdiff(I1, I2, s1); // |I1 - I2|
s1.convertTo(s1, CV_32F); // cannot make a square on 8 bits
s1 = s1.mul(s1); // |I1 - I2|^2
Scalar s = sum(s1); // sum elements per channel
double sse = s.val[0] + s.val[1] + s.val[2]; // sum channels
if( sse <= 1e-10) // for small values return zero
return 0;
else
{
double mse =sse /(double)(I1.channels() * I1.total());
double psnr = 10.0*log10((255*255)/mse);
return psnr;
}
}
Scalar getMSSIM( const Mat& i1, const Mat& i2)
{
const double C1 = 6.5025, C2 = 58.5225;
/***************************** INITS **********************************/
int d = CV_32F;
Mat I1, I2;
i1.convertTo(I1, d); // cannot calculate on one byte large values
i2.convertTo(I2, d);
Mat I2_2 = I2.mul(I2); // I2^2
Mat I1_2 = I1.mul(I1); // I1^2
Mat I1_I2 = I1.mul(I2); // I1 * I2
/*************************** END INITS **********************************/
Mat mu1, mu2; // PRELIMINARY COMPUTING
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 = average of ssim map
return mssim;
}
double ssim(Mat &i1, 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 I1_2 = I1.mul(I1);
Mat I2_2 = I2.mul(I2);
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, sigam2_2, sigam12;
GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);
sigma1_2 -= mu1_2;
GaussianBlur(I2_2, sigam2_2, Size(11, 11), 1.5);
sigam2_2 -= mu2_2;
GaussianBlur(I1_I2, sigam12, Size(11, 11), 1.5);
sigam12 -= mu1_mu2;
Mat t1, t2, t3;
t1 = 2 * mu1_mu2 + C1;
t2 = 2 * sigam12 + C2;
t3 = t1.mul(t2);
t1 = mu1_2 + mu2_2 + C1;
t2 = sigma1_2 + sigam2_2 + C2;
t1 = t1.mul(t2);
Mat ssim_map;
divide(t3, t1, ssim_map);
Scalar mssim = mean(ssim_map);
double ssim = (mssim.val[0] + mssim.val[1] + mssim.val[2]) /3;
return ssim;
}
int main()
{
ifstream infile("SiEi_6.txt");
int LUT[256][256];
string s;
int i = 0;
while(getline(infile, s)) {
if (!s.empty()) {
vector<string> v;
split(s, back_inserter(v));
for (int j = 0; j < 256; ++j)
LUT[i][j] = stoi(v[j]);
}
i++;
}
// 生成高斯操作核
static const int ksize = 3;
double window[ksize][ksize];
int window_int[ksize][ksize];
static const double sigma = 1;
static const double pi = 3.1415926;
int sum = 0;
int center = ksize / 2; // 模板的中心位置，也就是坐标的原点
double x2, y2;
for (int i = 0; i < ksize; i++) {
x2 = pow(i - center, 2);
for (int j = 0; j < ksize; j++) {
y2 = pow(j - center, 2);
double g = exp(-(x2 + y2) / (2 * sigma * sigma));
g /= 2 * pi * sigma * sigma;
window[i][j] = g;
}
}
double k = 1 / window[0][0]; // 将左上角的系数归一化为1
for (int i = 0; i < ksize; i++) {
for (int j = 0; j < ksize; j++) {
window[i][j] *= k;
window_int[i][j] = round(window[i][j]); // 如果左上角是0，需要对double类型用round()函数四舍五入
sum += window_int[i][j];
cout << window_int[i][j] << "\t";
}
cout << endl;
}
cout << sum << endl;
Mat src = imread("lena512.bmp", IMREAD_GRAYSCALE); //从文件中加载灰度图像
Mat dst = src.clone();
// 高斯滤波
for (int nrow = center; nrow < src.rows-center; nrow++) {
for (int ncol = center; ncol < src.cols-center; ncol++) {
int point = 0;
for (int i = 0; i < ksize; i++) {
for (int j = 0; j < ksize; j++) {
point += LUT[window_int[i][j]][src.ptr<uchar>(nrow+i-center)[ncol+j-center]];
// point += window_int[i][j] * src.ptr<uchar>(nrow+i-center)[ncol+j-center];
}
}
dst.ptr<uchar>(nrow)[ncol] = point/sum;
}
}
// imwrite("gas.bmp", dst);
// 采用Unsharpen Mask算法锐化
Mat usm;
addWeighted(src, 1.5, dst, -0.5, 0, usm);
imshow("src", src);
imshow("dst", dst);
imshow("usm", usm);
cout << "dst psnr " << getPSNR(src, dst) << endl;
cout << "dst ssim " << ssim(src, dst) * 3 * 100 << endl;
cout << "usm psnr " << getPSNR(src, usm) << endl;
cout << "usm ssim " << ssim(src, usm) * 3 * 100 << endl;
waitKey();
return 0;
}