OpenCV中的同態過濾

我正在嘗試使用高斯LPF編寫同態過濾的代碼，但結果我在最後形成了一個完整的黑色圖像。代碼的書面過濾器部分對其他應用程序完美無缺！OpenCV中的同態過濾

#include "stdafx.h" 
#include <opencv2/core/core.hpp> 
#include <opencv2\highgui\highgui.hpp> 
#include <opencv2/imgproc/imgproc.hpp> 
#include <iostream> 
using namespace std; 
using namespace cv; 

int main() 
{ 
    // Variables ======================================================================================== 
    int D0_GHPF = 80; // Gaussian HPF cut-off deviation 
    // ================================================================================================== 
    // Getting the frequency and magnitude of image ===================================================== 
    cv::Mat image = cv::imread("test2.tif", CV_LOAD_IMAGE_GRAYSCALE); 
    if(image.empty()) 
     return -1; 

    image.convertTo(image, CV_32F); 
    image += 1; 
    log(image,image); 
    cv::Mat padded1;       
    int m1 = cv::getOptimalDFTSize(image.rows); 
    int n1 = cv::getOptimalDFTSize(image.cols); 
    cv::copyMakeBorder(image, padded1, 0, m1 - image.rows, 0, n1 - image.cols, cv::BORDER_CONSTANT, cv::Scalar::all(0)); 

    cv::Mat image_planes[] = {cv::Mat_<float>(padded1), cv::Mat::zeros(padded1.size(), CV_32F)}; 
    cv::Mat image_complex; 
    cv::merge(image_planes, 2, image_complex);   

    cv::dft(image_complex, image_complex); 
    cv::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] 
    cv::Mat image_phase; 
    cv::phase(image_planes[0], image_planes[1], image_phase); 
    cv::Mat image_mag; 
    cv::magnitude(image_planes[0], image_planes[1], image_mag); 

    // Shifting the DFT 
    image_mag = image_mag(cv::Rect(0, 0, image_mag.cols & -2, image_mag.rows & -2)); 
    int cx = image_mag.cols/2; 
    int cy = image_mag.rows/2; 


    cv::Mat q0(image_mag, cv::Rect(0, 0, cx, cy)); 
    cv::Mat q1(image_mag, cv::Rect(cx, 0, cx, cy)); 
    cv::Mat q2(image_mag, cv::Rect(0, cy, cx, cy)); 
    cv::Mat q3(image_mag, cv::Rect(cx, cy, cx, cy)); 

    cv::Mat tmp; 
    q0.copyTo(tmp); 
    q3.copyTo(q0); 
    tmp.copyTo(q3); 

    q1.copyTo(tmp); 
    q2.copyTo(q1); 
    tmp.copyTo(q2); 

    // Creating GHPF ==================================================================================== 
    cv::Mat GHPF(image_mag.size(), CV_32F, 255); 

    float tempVal = float((-1.0)/float(pow(float(D0_GHPF),2))); 
    for (int i=0; i < GHPF.rows; i++) 
     for (int j=0; j < GHPF.cols; j++) 
     { 
      float dummy2 = float(pow(float(i - cy), 2) + pow(float(j - cx), 2)); 
      dummy2 = (2.0 - 0.25) * (1.0 - float(exp(float(dummy2 * tempVal)))) + 0.25; 
      GHPF.at<float>(i,j) = 255 * dummy2; 
     } 
    cv::normalize(GHPF, GHPF, 0, 1, CV_MINMAX); 
    cv::imshow("test", GHPF); 
    cv::waitKey(0); 
    // Applying GHPF filter ================================================================================== 
    cv::Mat GHPF_result(image_mag.size(), CV_32F); 
    cv::multiply(image_mag, GHPF, GHPF_result); 

    // reversing the shift ============================================================================== 
    cv::Mat q0_GHPF(GHPF_result, cv::Rect(0, 0, cx, cy)); 
    cv::Mat q1_GHPF(GHPF_result, cv::Rect(cx, 0, cx, cy)); 
    cv::Mat q2_GHPF(GHPF_result, cv::Rect(0, cy, cx, cy)); 
    cv::Mat q3_GHPF(GHPF_result, cv::Rect(cx, cy, cx, cy)); 

    cv::Mat tmp_GHPF; 
    q0_GHPF.copyTo(tmp_GHPF); 
    q3_GHPF.copyTo(q0_GHPF); 
    tmp_GHPF.copyTo(q3_GHPF); 

    q1_GHPF.copyTo(tmp_GHPF); 
    q2_GHPF.copyTo(q1_GHPF); 
    tmp_GHPF.copyTo(q2_GHPF); 

    // Reconstructing the image with new GHPF filter ==================================================== 
    cv::Mat GHPFresult_planes[2]; 
    cv::polarToCart(GHPF_result, image_phase,GHPFresult_planes[0], GHPFresult_planes[1]); 

    cv::Mat GHPFresult_complex; 
    cv::merge(GHPFresult_planes,2,GHPFresult_complex); 

    //calculating the iDFT for GHPF 
    cv::Mat GHPF_inverseTransform; 
    cv::dft(GHPFresult_complex, GHPF_inverseTransform, cv::DFT_INVERSE|cv::DFT_REAL_OUTPUT); 

    exp(GHPF_inverseTransform,GHPF_inverseTransform); 
    cv::normalize(GHPF_inverseTransform, GHPF_inverseTransform, 0, 1, CV_MINMAX); 
    cv::imshow("GHPF Reconstructed", GHPF_inverseTransform); 
    cv::waitKey(0); 
}

的理論基礎是岡薩雷斯第3版，

來源

2013-11-22 Dark Sauron

好，我有一個很難與同態濾波，過數字圖像處理的章節。，我發現有一些錯誤代碼，當談到EXP：#INF

cv::normalize(GHPF_inverseTransform, GHPF_inverseTransform, 0, 1, CV_MINMAX); 
cv::exp(GHPF_inverseTransform, GHPF_inverseTransform); 
cv::normalize(GHPF_inverseTransform, GHPF_inverseTransform, 0,255, CV_MINMAX);

這樣的，不同的東西發生。然後

cv::normalize(GHPF_inverseTransform, GHPF_inverseTransform, 0, 0.000001, CV_MINMAX); 
cv::exp(GHPF_inverseTransform, GHPF_inverseTransform); 
cv::normalize(GHPF_inverseTransform, GHPF_inverseTransform, 100,255, CV_MINMAX);

這個時候，我可以清楚地看到圖像（很好，但它看起來像我的世界的圖像）所以我複製另一個GHPF它完美地工作。

所以我想你的GHPF仍然有問題。（對不起，因爲我的英語差：p）

來源

2014-03-05 06:35:26 Jarvet

OpenCV中的同態過濾

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