Goal

In this tutorial you will learn:

what a degradation image model is
what the PSF of an out-of-focus image is
how to restore a blurred image
what is a Wiener filter

Theory

Note: The explanation is based on the books [109] and [329]. Also, you can refer to Matlab's tutorial Image Deblurring in Matlab and the article SmartDeblur.; The out-of-focus image on this page is a real world image. The out-of-focus was achieved manually by camera optics.

What is a degradation image model?

Here is a mathematical model of the image degradation in frequency domain representation:

$S = H \cdot U + N$

where $S$ is a spectrum of blurred (degraded) image, $U$ is a spectrum of original true (undegraded) image, $H$ is a frequency response of point spread function (PSF), $N$ is a spectrum of additive noise.

The circular PSF is a good approximation of out-of-focus distortion. Such a PSF is specified by only one parameter - radius $R$ . Circular PSF is used in this work.

Circular point spread function

How to restore a blurred image?

The objective of restoration (deblurring) is to obtain an estimate of the original image. The restoration formula in frequency domain is:

$U^{'} = H_{w} \cdot S$

where $U^{'}$ is the spectrum of estimation of original image $U$ , and $H_{w}$ is the restoration filter, for example, the Wiener filter.

What is the Wiener filter?

The Wiener filter is a way to restore a blurred image. Let's suppose that the PSF is a real and symmetric signal, a power spectrum of the original true image and noise are not known, then a simplified Wiener formula is:

$H_{w} = \frac{H}{| H |^{2} + \frac{1}{S N R}}$

where $S N R$ is signal-to-noise ratio.

So, in order to recover an out-of-focus image by Wiener filter, it needs to know the $S N R$ and $R$ of the circular PSF.

Source code

You can find source code in the samples/cpp/tutorial_code/ImgProc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.cpp of the OpenCV source code library.

 
#include <iostream>
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
 
using namespace cv;
using namespace std;
 
void help();
void calcPSF(Mat& outputImg, Size filterSize, int R);
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);
 
const String keys =
"{help h usage ? |             | print this message   }"
"{image          |original.jpg | input image name     }"
"{R              |5           | radius               }"
"{SNR            |100         | 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 R = parser.get<int>("R");
    int snr = parser.get<int>("SNR");
    string strInFileName = parser.get<String>("image");
    samples::addSamplesDataSearchSubDirectory("doc/tutorials/imgproc/out_of_focus_deblur_filter/images");
 
    if (!parser.check())
    {
        parser.printErrors();
        return 0;
    }
 
    Mat imgIn;
    imgIn = imread(samples::findFile( 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 only
    Rect roi = Rect(0, 0, imgIn.cols & -2, imgIn.rows & -2);
 
    //Hw calculation (start)
    Mat Hw, h;
    calcPSF(h, roi.size(), R);
    calcWnrFilter(h, Hw, 1.0 / double(snr));
    //Hw calculation (stop)
 
    // filtering (start)
    filter2DFreq(imgIn(roi), imgOut, Hw);
    // filtering (stop)
 
    imgOut.convertTo(imgOut, CV_8U);
    normalize(imgOut, imgOut, 0, 255, NORM_MINMAX);
    imshow("Original", imgIn);
    imshow("Debluring", imgOut);
    imwrite("result.jpg", imgOut);
    waitKey(0);
    return 0;
}
 
void help()
{
    cout << "2018-07-12" << endl;
    cout << "DeBlur_v8" << endl;
    cout << "You will learn how to recover an out-of-focus image by Wiener filter" << endl;
}
 
void calcPSF(Mat& outputImg, Size filterSize, int R)
{
    Mat h(filterSize, CV_32F, Scalar(0));
    Point point(filterSize.width / 2, filterSize.height / 2);
    circle(h, point, R, 255, -1, 8);
    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);
}

Explanation

An out-of-focus image recovering algorithm consists of PSF generation, Wiener filter generation and filtering a blurred image in frequency domain:

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

A function calcPSF() forms a circular PSF according to input parameter radius $R$ :

void calcPSF(Mat& outputImg, Size filterSize, int R)
{
    Mat h(filterSize, CV_32F, Scalar(0));
    Point point(filterSize.width / 2, filterSize.height / 2);
    circle(h, point, R, 255, -1, 8);
    Scalar summa = sum(h);
    outputImg = h / summa[0];
}

A function calcWnrFilter() synthesizes the simplified Wiener filter $H_{w}$ according to the formula described above:

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);
}

A function fftshift() rearranges the PSF. This code was just copied from the tutorial Discrete Fourier Transform:

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);
}

A function filter2DFreq() filters the blurred image in the frequency domain:

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];
}

Result

Below you can see the real out-of-focus image:

And the following result has been computed with $R$ = 53 and $S N R$ = 5200 parameters:

The Wiener filter was used, and values of $R$ and $S N R$ were selected manually to give the best possible visual result. We can see that the result is not perfect, but it gives us a hint to the image's content. With some difficulty, the text is readable.

Note: The parameter $R$ is the most important. So you should adjust $R$ first, then $S N R$ .; Sometimes you can observe the ringing effect in a restored image. This effect can be reduced with several methods. For example, you can taper input image edges.

You can also find a quick video demonstration of this on YouTube.

References

Image Deblurring in Matlab - Image Deblurring in Matlab
SmartDeblur - SmartDeblur site


Original author	Karpushin Vladislav
Compatibility	OpenCV >= 3.0

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