模糊前
反模糊
源码实现:
#include <iostream>
#include <opencv2/opencv.hpp>
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
using namespace cv;
using namespace std;
void calcPSF(Mat& outputImg, Size filterSize, int len, double theta);
void fftshift(const Mat& inputImg, Mat& outputImg);
void filter2DFreq(const Mat& inputImg, Mat& outputImg, const Mat& H);
void calcWnrFilter(const Mat& input_h_PSF, Mat& output_G, double nsr);
void edgetaper(const Mat& inputImg, Mat& outputImg, double gamma = 5.0, double beta = 0.2);
int adjust_len = 10;
void adjust_filter(int, void*);
int THETA =53;
int snr = 5200;//SNR表示信噪比
Mat src = imread("original.jpg", IMREAD_GRAYSCALE);
int main(int argc, char *argv[])
{
if (src.empty())
{
printf("ERROR : Image cannot be loaded..!!");
return -1;
}
imshow("input", src);
namedWindow("result", WINDOW_AUTOSIZE);
createTrackbar("Adjust R:", "result", &adjust_len, 60, adjust_filter);
createTrackbar("THETA:", "result", &THETA, 360, adjust_filter);
createTrackbar("snr:", "result", &snr, 10000,adjust_filter);
Mat imgOut;
Rect roi = Rect(0, 0, src.cols & -2, src.rows & -2);
Mat Hw, h;
calcPSF(h, roi.size(), adjust_len, THETA);
calcWnrFilter(h, Hw, 1.0 / double(snr));
src.convertTo(src, CV_32F);
edgetaper(src, src);
filter2DFreq(src(roi), imgOut, Hw);
imgOut.convertTo(imgOut, CV_8U);
normalize(imgOut, imgOut, 0, 255, NORM_MINMAX);
imwrite("motion_deblur_result.jpg", imgOut);
imshow("result", imgOut);
waitKey(0);
return 0;
}
//
void adjust_filter(int, void*) {
Mat imgOut;
////偶数处理
Rect roi = Rect(0, 0, src.cols & -2, src.rows & -2);
Mat Hw, h;
//生成PSF与维纳滤波器
calcPSF(h, roi.size(), adjust_len, THETA);
calcWnrFilter(h, Hw, 1.0 / double(snr));
src.convertTo(src, CV_32F);
edgetaper(src, src);
//反模糊
filter2DFreq(src(roi), imgOut, Hw);
//归一化显示
imgOut.convertTo(imgOut, CV_8U);
normalize(imgOut, imgOut, 0, 255, NORM_MINMAX);
imwrite("motion_deblur_result.jpg", imgOut);
imshow("result", imgOut);
}
//计算PSF
//https://blog.csdn.net/qq254612999/article/details/50509793
void calcPSF(Mat& outputImg, Size filterSize, int len, double theta)
{
Mat h(filterSize, CV_32F, Scalar(0));
Point point(filterSize.width / 2, filterSize.height / 2);
ellipse(h, point, Size(0, cvRound(float(len) / 2.0)), 90.0 - theta, 0, 360, Scalar(255), FILLED);
Scalar summa = sum(h);
outputImg = h / summa[0];
}
//图像傅里叶变换
//https://www.jianshu.com/p/89ce7fdb9e12
void fftshift(const Mat& inputImg, Mat& outputImg)
{
outputImg = inputImg.clone();
int cx = outputImg.cols / 2;
int cy = outputImg.rows / 2;
Mat q0(outputImg, Rect(0, 0, cx, cy));
Mat q1(outputImg, Rect(cx, 0, cx, cy));
Mat q2(outputImg, Rect(0, cy, cx, cy));
Mat q3(outputImg, Rect(cx, cy, cx, cy));
Mat tmp;
q0.copyTo(tmp);
q3.copyTo(q0);
tmp.copyTo(q3);
q1.copyTo(tmp);
q2.copyTo(q1);
tmp.copyTo(q2);
}
//实现反模糊
void filter2DFreq(const Mat& inputImg, Mat& outputImg, const Mat& H)
{
Mat planes[2] = { Mat_<float>(inputImg.clone()), Mat::zeros(inputImg.size(), CV_32F) };
Mat complexI;
merge(planes, 2, complexI);
dft(complexI, complexI, DFT_SCALE);
Mat planesH[2] = { Mat_<float>(H.clone()), Mat::zeros(H.size(), CV_32F) };
Mat complexH;
merge(planesH, 2, complexH);
Mat complexIH;
mulSpectrums(complexI, complexH, complexIH, 0);
idft(complexIH, complexIH);
split(complexIH, planes);
outputImg = planes[0];
}
//生成维纳滤波算法
//https://wenku.baidu.com/view/76bd5f36f111f18583d05a00.html
void calcWnrFilter(const Mat& input_h_PSF, Mat& output_G, double nsr)
{
Mat h_PSF_shifted;
fftshift(input_h_PSF, h_PSF_shifted);
Mat planes[2] = { Mat_<float>(h_PSF_shifted.clone()), Mat::zeros(h_PSF_shifted.size(), CV_32F) };
Mat complexI;
merge(planes, 2, complexI);
dft(complexI, complexI);
split(complexI, planes);
Mat denom;
pow(abs(planes[0]), 2, denom);
denom += nsr;
divide(planes[0], denom, output_G);
}
void edgetaper(const Mat& inputImg, Mat& outputImg, double gamma, double beta)
{
int Nx = inputImg.cols;
int Ny = inputImg.rows;
Mat w1(1, Nx, CV_32F, Scalar(0));
Mat w2(Ny, 1, CV_32F, Scalar(0));
float* p1 = w1.ptr<float>(0);
float* p2 = w2.ptr<float>(0);
float dx = float(2.0 * CV_PI / Nx);
float x = float(-CV_PI);
for (int i = 0; i < Nx; i++)
{
p1[i] = float(0.5 * (tanh((x + gamma / 2) / beta) - tanh((x - gamma / 2) / beta)));
x += dx;
}
float dy = float(2.0 * CV_PI / Ny);
float y = float(-CV_PI);
for (int i = 0; i < Ny; i++)
{
p2[i] = float(0.5 * (tanh((y + gamma / 2) / beta) - tanh((y - gamma / 2) / beta)));
y += dy;
}
Mat w = w2 * w1;
multiply(inputImg, w, outputImg);
}
缺陷:
参数len.n.snr需要调节到合适数值,才能实现理想效果,因此博主加个三个调节阀。