OpenCV4.9去运动模糊滤镜(68)-编程知识

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目标

在本教程中，您将学习：

运动模糊图像的 PSF 是多少
如何恢复运动模糊图像

理论

对于退化图像模型理论和维纳滤波理论，您可以参考教程失焦去模糊滤镜。在此页面上，仅考虑线性运动模糊失真。此页面上的运动模糊图像是真实世界的图像。模糊是由移动的主体引起的。

运动模糊图像的 PSF 是多少？

线性运动模糊失真的点扩散函数(PSF) 是线段。这样的 PSF 由两个参数指定：(LEN)是模糊的长度，(THETA)是运动角度。

线性运动模糊失真的点扩散函数

如何恢复模糊的图像？

在此页面上，Wiener 滤镜用作恢复滤镜，有关详细信息，您可以参考教程失焦去模糊滤镜。为了在运动模糊情况下合成维纳滤波器，它需要指定PSF的信噪比(SNR)、(LEN)和(THETA)。

源代码

您可以在 OpenCV 源代码库中找到源代码。samples/cpp/tutorial_code/ImgProc/motion_deblur_filter/motion_deblur_filter.cpp

#include <iostream>
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"using namespace cv;
using namespace std;void help();
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);const String keys =
"{help h usage ? | | print this message }"
"{image |input.png | input image name }"
"{LEN |125 | length of a motion }"
"{THETA |0 | angle of a motion in degrees }"
"{SNR |700 | signal to noise ratio }"
;int main(int argc, char *argv[])
{help();CommandLineParser parser(argc, argv, keys);if (parser.has("help")){parser.printMessage();return 0;}int LEN = parser.get<int>("LEN");double THETA = parser.get<double>("THETA");int snr = parser.get<int>("SNR");string strInFileName = parser.get<String>("image");if (!parser.check()){parser.printErrors();return 0;}Mat imgIn;imgIn = imread(strInFileName, IMREAD_GRAYSCALE);if (imgIn.empty()) //check whether the image is loaded or not{cout << "ERROR : Image cannot be loaded..!!" << endl;return -1;}Mat imgOut;// it needs to process even image onlyRect roi = Rect(0, 0, imgIn.cols & -2, imgIn.rows & -2);//Hw calculation (start)Mat Hw, h;calcPSF(h, roi.size(), LEN, THETA);calcWnrFilter(h, Hw, 1.0 / double(snr));//Hw calculation (stop)imgIn.convertTo(imgIn, CV_32F);edgetaper(imgIn, imgIn);// filtering (start)filter2DFreq(imgIn(roi), imgOut, Hw);// filtering (stop)imgOut.convertTo(imgOut, CV_8U);normalize(imgOut, imgOut, 0, 255, NORM_MINMAX);imwrite("result.jpg", imgOut);return 0;
}void help()
{cout << "2018-08-14" << endl;cout << "Motion_deblur_v2" << endl;cout << "You will learn how to recover an image with motion blur distortion using a Wiener filter" << endl;
}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];
}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);
}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];
}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);
}

解释

运动模糊图像恢复算法包括 PSF 生成、Wiener 滤波器生成和滤除频域中的模糊图像：

 // it needs to process even image onlyRect roi = Rect(0, 0, imgIn.cols & -2, imgIn.rows & -2);//Hw calculation (start)Mat Hw, h;calcPSF(h, roi.size(), LEN, THETA);calcWnrFilter(h, Hw, 1.0 / double(snr));//Hw calculation (stop)imgIn.convertTo(imgIn, CV_32F);edgetaper(imgIn, imgIn);// filtering (start)filter2DFreq(imgIn(roi), imgOut, Hw);// filtering (stop)

函数 edgetaper()会逐渐缩小输入图像的边缘，以减少恢复图像中的振铃效应：

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);
}

函数 calcWnrFilter()、fftshift()和 filter2DFreq()在频域中通过指定的 PSF 实现图像滤波。这些函数是从教程失焦去模糊滤镜中复制的。.