OpenCV：同态滤波器

我想使用同态滤波器来处理水下图像。我尝试用互联网上找到的代码对其进行编码，但我总是有一个黑色图像...我尝试标准化我的结果，但没有成功。

这是我的功能：

void HomomorphicFilter::butterworth_homomorphic_filter(Mat &dft_Filter, int D, int n, float high_h_v_TB, float low_h_v_TB)
{
Mat single(dft_Filter.rows, dft_Filter.cols, CV_32F);
Point centre = Point(dft_Filter.rows/2, dft_Filter.cols/2);
double radius;
float upper = (high_h_v_TB * 0.01);
float lower = (low_h_v_TB * 0.01);

//create essentially create a butterworth highpass filter
//with additional scaling and offset
for(int i = 0; i < dft_Filter.rows; i++)
{
    for(int j = 0; j < dft_Filter.cols; j++)
    {
        radius = (double) sqrt(pow((i - centre.x), 2.0) + pow((double) (j - centre.y), 2.0));
        single.at<float>(i,j) =((upper - lower) * (1/(1 + pow((double) (D/radius), (double) (2*n))))) + lower;
    }
}
//normalize(single, single, 0, 1, CV_MINMAX);
//Apply filter
mulSpectrums( dft_Filter, single, dft_Filter, 0);
}

void HomomorphicFilter::Shifting_DFT(Mat &fImage)
{
//For visualization purposes we may also rearrange the quadrants of the result, so that the origin (0,0), corresponds to the image center.
Mat tmp, q0, q1, q2, q3;

/*First crop the image, if it has an odd number of rows or columns.
Operator & bit to bit by -2 (two's complement : -2 = 111111111....10) to eliminate the first bit 2^0 (In case of odd number on row or col, we take the even number in below)*/
fImage = fImage(Rect(0, 0, fImage.cols & -2, fImage.rows & -2));
int cx = fImage.cols/2;
int cy = fImage.rows/2;

/*Rearrange the quadrants of Fourier image so that the origin is at the image center*/
q0 = fImage(Rect(0, 0, cx, cy));
q1 = fImage(Rect(cx, 0, cx, cy));
q2 = fImage(Rect(0, cy, cx, cy));
q3 = fImage(Rect(cx, cy, cx, cy));

/*We reverse each quadrant of the frame with its other quadrant diagonally opposite*/
/*We reverse q0 and q3*/
q0.copyTo(tmp);
q3.copyTo(q0);
tmp.copyTo(q3);

/*We reverse q1 and q2*/
q1.copyTo(tmp);
q2.copyTo(q1);
tmp.copyTo(q2);
}

void HomomorphicFilter::Fourier_Transform(Mat frame_bw, Mat &image_phase, Mat &image_mag)
{
Mat frame_log;
frame_bw.convertTo(frame_log, CV_32F);
/*Take the natural log of the input (compute log(1 + Mag)*/
frame_log += 1;
log( frame_log, frame_log); // log(1 + Mag)

/*2. Expand the image to an optimal size
The performance of the DFT depends of the image size. It tends to be the fastest for image sizes that are multiple of 2, 3 or 5.
We can use the copyMakeBorder() function to expand the borders of an image.*/
Mat padded;
int M = getOptimalDFTSize(frame_log.rows);
int N = getOptimalDFTSize(frame_log.cols);
copyMakeBorder(frame_log, padded, 0, M - frame_log.rows, 0, N - frame_log.cols, BORDER_CONSTANT, Scalar::all(0));

/*Make place for both the complex and real values
The result of the DFT is a complex. Then the result is 2 images (Imaginary + Real), and the frequency domains range is much larger than the spatial one. Therefore we need to store in float !
That's why we will convert our input image "padded" to float and expand it to another channel to hold the complex values.
Planes is an arrow of 2 matrix (planes[0] = Real part, planes[1] = Imaginary part)*/
Mat image_planes[] = {Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F)};
Mat image_complex;
/*Creates one multichannel array out of several single-channel ones.*/
merge(image_planes, 2, image_complex);

/*Make the DFT
The result of thee DFT is a complex image : "image_complex"*/
dft(image_complex, image_complex);

/***************************/
//Create spectrum magnitude//
/***************************/
/*Transform the real and complex values to magnitude
NB: We separe Real part to Imaginary part*/
split(image_complex, image_planes);
//Starting with this part we have the real part of the image in planes[0] and the imaginary in planes[1]
phase(image_planes[0], image_planes[1], image_phase);
magnitude(image_planes[0], image_planes[1], image_mag);
}

void HomomorphicFilter::Inv_Fourier_Transform(Mat image_phase, Mat image_mag, Mat &inverseTransform)
{
/*Calculates x and y coordinates of 2D vectors from their magnitude and angle.*/
Mat result_planes[2];
polarToCart(image_mag, image_phase, result_planes[0], result_planes[1]);

/*Creates one multichannel array out of several single-channel ones.*/
Mat result_complex;
merge(result_planes, 2, result_complex);

/*Make the IDFT*/
dft(result_complex, inverseTransform, DFT_INVERSE|DFT_REAL_OUTPUT);

/*Take the exponential*/
exp(inverseTransform, inverseTransform);
}

这是我的主要代码：

    /**************************/
    /****Homomorphic filter****/
    /**************************/
    /**********************************************/
    //Getting the frequency and magnitude of image//
    /**********************************************/
    Mat image_phase, image_mag;
    HomomorphicFilter().Fourier_Transform(frame_bw, image_phase, image_mag);
    /******************/
    //Shifting the DFT//
    /******************/
    HomomorphicFilter().Shifting_DFT(image_mag);
    /********************************/
    //Butterworth homomorphic filter//
    /********************************/
    int high_h_v_TB = 101;
    int low_h_v_TB = 99;
    int D = 10;// radius of band pass filter parameter
    int order = 2;// order of band pass filter parameter
    HomomorphicFilter().butterworth_homomorphic_filter(image_mag, D, order, high_h_v_TB, low_h_v_TB);
    /******************/
    //Shifting the DFT//
    /******************/
    HomomorphicFilter().Shifting_DFT(image_mag);
    /*******************************/
    //Inv Discret Fourier Transform//
    /*******************************/
    Mat inverseTransform;
    HomomorphicFilter().Inv_Fourier_Transform(image_phase, image_mag, inverseTransform);
    imshow("Result", inverseTransform);

如果有人可以解释我的错误，我将不胜感激:)。谢谢你，并对我糟糕的英语感到抱歉。

编辑：现在，我有一些东西，但它并不完美......我修改了代码中的两件事。 我在 dft 之后应用了 log(mag + 1)，而不是在输入图像上。 我在 idft 之后删除了 exp() 。

这是结果（我只能发布 2 个链接...）：

my input image : final result :

在看过几个主题后，我在巴特沃斯滤波器上和 dft/shifting 后的幅度上发现了类似的结果。 不幸的是，我的最终结果不是很好。为什么我有这么多“噪音”？

当更换相机导致图像变暗时，我正在使用这种方法来平衡照明！

我尝试对频率进行 FFT 来过滤图像！它可以工作。但是花费了太多时间。（2750 * 3680RGB图像）。所以我在空间域中进行。

这是我的代码！

//IplImage *imgSrcI=cvLoadImage("E:\\lean.jpg",-1);
Mat imgSrcM(imgSrc,true);
Mat imgDstM;

Mat imgGray;
Mat imgHls;
vector<Mat> vHls;

Mat imgTemp1=Mat::zeros(imgSrcM.size(),CV_64FC1);
Mat imgTemp2=Mat::zeros(imgSrcM.size(),CV_64FC1);

if(imgSrcM.channels()==1)
{
    imgGray=imgSrcM.clone();
}
else if (imgSrcM.channels()==3)
{
    cvtColor(imgSrcM, imgHls, CV_BGR2HLS);
    split(imgHls, vHls);
    imgGray=vHls.at(1);
}
else
{
    return -1;
}
imgGray.convertTo(imgTemp1,CV_64FC1);
imgTemp1=imgTemp1+0.0001;
log(imgTemp1,imgTemp1);

GaussianBlur(imgTemp1, imgTemp2, Size(21, 21), 0.1, 0.1, BORDER_DEFAULT);//imgTemp2是低通滤波的结果
imgTemp1 = (imgTemp1 - imgTemp2);//imgTemp1是对数减低通的高通
addWeighted(imgTemp2, 0.7, imgTemp1, 1.4, 1, imgTemp1, -1);//imgTemp1是压制低频增强高频的结构

exp(imgTemp1,imgTemp1);
normalize(imgTemp1,imgTemp1,0,1,NORM_MINMAX);
imgTemp1=imgTemp1*255;

imgTemp1.convertTo(imgGray, CV_8UC1);

//imwrite("E:\\leanImgGray.jpg",imgGray);
if (imgSrcM.channels()==3)
{
    vHls.at(1)=imgGray;
    merge(vHls,imgHls);
    cvtColor(imgHls, imgDstM, CV_HLS2BGR);

}
else if (imgSrcM.channels()==1)
{
    imgDstM=imgGray.clone();
}

cvCopy(&(IplImage)imgDstM,imgDst);
//cvShowImage("jpg",imgDst);

return 0;