我想表明图像的相位比其幅度更多地传递信息，所以我想交换两个图像的幅度然后进行逆DFT。 这是代码：
void main()
{
Mat I1 = imread("lena.jpg", CV_LOAD_IMAGE_GRAYSCALE);
Mat I2 = imread("peppers.png", CV_LOAD_IMAGE_GRAYSCALE);
Mat padded1,padded2;
//expand input image to optimal size
int m1= getOptimalDFTSize( I1.rows );
int n1 = getOptimalDFTSize( I1.cols );
int m2= getOptimalDFTSize( I2.rows );
int n2 = getOptimalDFTSize( I2.cols );
// on the border add zero values
copyMakeBorder(I1, padded1, 0, m1 - I1.rows, 0, n1 - I1.cols, BORDER_CONSTANT, Scalar::all(0));
copyMakeBorder(I2, padded2, 0, m2 - I2.rows, 0, n2 - I2.cols, BORDER_CONSTANT, Scalar::all(0));
Mat planes1[] = {Mat_<float>(padded1), Mat::zeros(padded1.size(), CV_32F)};
Mat planes2[] = {Mat_<float>(padded2), Mat::zeros(padded2.size(), CV_32F)};
Mat complexI, complexII;
// Add to the expanded another plane with zeros
merge(planes1, 2, complexI);
merge(planes2, 2, complexII);
dft(complexI, complexI);
dft(complexII, complexII);
// compute the magnitude and phase then switch to logarithmic scale
// => magnitude:log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2)), phase:arctan(Im(DFT(I)),Re(DFT(I)))
split(complexI, planes1);// planes[0] = Re(DFT(I)), planes[1] = Im(DFT(I))
Mat ph1, magI1;
phase(planes1[0], planes1[1], ph1);//ph1 = phase
magnitude(planes1[0], planes1[1], magI1);// magI1 = magnitude
magI1 = magI1(Rect(0, 0, magI1.cols & -2, magI1.rows & -2));
ph1 = ph1(Rect(0, 0, ph1.cols & -2, ph1.rows & -2));
split(complexII, planes2);// planes[0] = Re(DFT(I)), planes[1] = Im(DFT(I))
Mat ph2, magI2;
phase(planes2[0], planes2[1], ph2);//ph2 = phase
magnitude(planes2[0], planes2[1], magI2);// mag2 = magnitude
magI2 = magI2(Rect(0, 0, magI2.cols & -2, magI2.rows & -2));
ph2 = ph2(Rect(0, 0, ph2.cols & -2, ph2.rows & -2));
planes1[1] = ph1; planes1[0] = magI2;
planes2[1] = ph2; planes2[0] = magI1;
dft(complexI,complexI,DFT_INVERSE);
dft(complexII,complexII,DFT_INVERSE);
imshow("image", complexI);
waitKey();
}
我只是将幅度和相位合并在一起，然后进行IDFT，似乎完全错误。
答案 0 :(得分：1)
我想你的问题是你的dft的某些东西不起作用。 尝试以下代码（添加分割平面后），看看它是否有效。 图像必须与Exact相同。 如果出现其他问题：请显示您的图片和结果。也许你的代码是正确的，你只是期待错误的东西。
以下是工作示例：
// Load an image
Mat I1 = imread("lena.jpg", CV_LOAD_IMAGE_GRAYSCALE);
Mat I2 = imread("peppers.png", CV_LOAD_IMAGE_GRAYSCALE);
Mat fI1;
Mat fI2;
I1.convertTo(fI1, CV_32F);
I2.convertTo(fI2, CV_32F);
//Perform DFT
Mat fourierTransform1;
Mat fourierTransform2;
dft(fI1, fourierTransform1, DFT_SCALE|DFT_COMPLEX_OUTPUT);
dft(fI2, fourierTransform2, DFT_SCALE|DFT_COMPLEX_OUTPUT);
//your split plane and everything else
//Perform IDFT
Mat inverseTransform1;
Mat inverseTransform2;
dft(fourierTransform1, inverseTransform1, DFT_INVERSE|DFT_REAL_OUTPUT);
dft(fourierTransform2, inverseTransform2, DFT_INVERSE|DFT_REAL_OUTPUT);
Mat result1;
Mat result2;
inverseTransform1.convertTo(result1, CV_8U);
inverseTransform2.convertTo(result2, CV_8U);
答案 1 :(得分：1)
您无法使用cv::merge()函数合并幅度和相位，而应使用cv::polarToCart()。这是我做你想做的代码：
using namespace cv;
// Rearrange the quadrants of a Fourier image so that the origin is at
// the image center
void shiftDFT(Mat &fImage )
{
Mat tmp, q0, q1, q2, q3;
// first crop the image, if it has an odd number of rows or columns
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));
q0.copyTo(tmp);
q3.copyTo(q0);
tmp.copyTo(q3);
q1.copyTo(tmp);
q2.copyTo(q1);
tmp.copyTo(q2);
}
int main()
{
// Load an image
Mat I1 = imread("lena.jpg", CV_LOAD_IMAGE_GRAYSCALE);
Mat I2 = imread("pepper.jpg", CV_LOAD_IMAGE_GRAYSCALE);
Mat fI1;
Mat fI2;
I1.convertTo(fI1, CV_32F);
I2.convertTo(fI2, CV_32F);
//expand input image to optimal size
int m = getOptimalDFTSize( I1.rows );
int n = getOptimalDFTSize( I1.cols );
Mat padded1, padded2;
// on the border add zero values
copyMakeBorder(fI1, padded1, 0, m - I1.rows, 0, n - I1.cols, BORDER_CONSTANT, Scalar::all(0));
copyMakeBorder(fI2, padded2, 0, m - I2.rows, 0, n - I2.cols, BORDER_CONSTANT, Scalar::all(0));
//Perform DFT
Mat fourierTransform1;
Mat fourierTransform2;
Mat planes1[2], planes2[2];
dft(fI1, fourierTransform1, DFT_SCALE|DFT_COMPLEX_OUTPUT);
dft(fI2, fourierTransform2, DFT_SCALE|DFT_COMPLEX_OUTPUT);
shiftDFT(fourierTransform1);
shiftDFT(fourierTransform2);
split(fourierTransform1, planes1);// planes[0] = Re(DFT(I)), planes[1] = Im(DFT(I))
split(fourierTransform2, planes2);// planes[0] = Re(DFT(I)), planes[1] = Im(DFT(I))
Mat ph1, mag1;
mag1.zeros(planes1[0].rows, planes1[0].cols, CV_32F);
ph1.zeros(planes1[0].rows, planes1[0].cols, CV_32F);
cartToPolar(planes1[0], planes1[1], mag1, ph1);
Mat ph2, mag2;
mag2.zeros(planes2[0].rows, planes2[0].cols, CV_32F);
ph2.zeros(planes2[0].rows, planes2[0].cols, CV_32F);
cartToPolar(planes2[0], planes2[1], mag2, ph2);
polarToCart(mag1, ph2, planes1[0], planes1[1]);
polarToCart(mag2, ph1, planes2[0], planes2[1]);
merge(planes1, 2, fourierTransform1);
merge(planes2, 2, fourierTransform2);
shiftDFT(fourierTransform1);
shiftDFT(fourierTransform2);
//Perform IDFT
Mat inverseTransform1, inverseTransform2;
dft(fourierTransform1, inverseTransform1, DFT_INVERSE|DFT_REAL_OUTPUT);
dft(fourierTransform2, inverseTransform2, DFT_INVERSE|DFT_REAL_OUTPUT);
namedWindow("original image 1");
imshow("original image 1", I1);
namedWindow("original image 2");
imshow("original image 2", I2);
waitKey(0);
cv::Mat out1, out2;
inverseTransform1.convertTo(out1, CV_8U);
inverseTransform2.convertTo(out2, CV_8U);
namedWindow("result image 1");
imshow("result image 1", out1);
namedWindow("result image 2");
imshow("result image 2", out2);
waitKey(0);
}