图像处理分析-FFT

((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2 + 1] = 0.0;
}else{
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2] = 0.0;
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2 + 1] = 0.0;
CvMat *sum_dst =cvCreateMat(dft_H,dft_W, CV_64FC2);//2 channels (dst_Re, dst_Im)
CvMat *sum_src = cvCreateMat(dft_H,dft_W, CV_64FC2 );
cvConvert(fourier, sum_src);
cvReleaseMat(&src_Im);
cvReleaseMat(&sum_src);
cvReleaseMat(&sum_dst);
return fourier;
}
//DFTLeabharlann Baidu变换
IplImage *IDFT(IplImage * fourier)
{
IplImage* dst = cvCreateImage(cvGetSize(fourier),IPL_DEPTH_8U,1);
// Compute the magnitude of the spectrum Mag = sqrt(Re^2 + Im^2)
cvPow( image_Re, image_Re, 2.0);
cvPow( image_Im, image_Im, 2.0);
cvAdd( image_Re, image_Im, image_Re);
CV_IMAGE_ELEM( dst, uchar, j, i) = CV_IMAGE_ELEM(dst, uchar, j+cy, i+cx);
CV_IMAGE_ELEM( dst, uchar, j+cy, i+cx) = tmp13;
tmp24 = CV_IMAGE_ELEM( dst, uchar, j, i+cx);
}else{
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2] = 1.0;
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2 + 1] = 0.0;
}
break;
case BW_LOW:
tempD = 1 / (1 + pow(tempD / D0, 2 * n));
for(j = 0; j < width; j++){
if(i > y && j > x){
state = 3;
}else if(i > y){
state = 1;
}else if(j > x){
state = 2;
}else{
state = 0;
}
switch(state){
case 0:
tempD = (double)sqrt(1.0*i * i + j * j);break;
int dft_H, dft_W;
dft_H = src->height;
dft_W = src->width;
CvMat *src_Re = cvCreateMat(dft_H,dft_W, CV_64FC1);// double Re, Im;
CvMat *src_Im = cvCreateMat(dft_H,dft_W, CV_64FC1);//Imaginary part
}
break;
case IDEAL_HIGH:
if(tempD <= D0){
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2] = 0.0;
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2 + 1] = 0.0;
cvDFT(sum_src,sum_dst,CV_DXT_INV_SCALE,0);
cvSplit(sum_dst,dst_Re,dst_Im,0,0);
cvConvert(dst_Re, dst);
cvReleaseMat(&dst_Re);
cvReleaseMat(&dst_Im);
cvReleaseMat(&sum_src);
cvReleaseMat(&sum_dst);
return dst;
}
//归一化，将灰度映射到0~255之间,并将能量最高的四角移到中心,生成图片频域能量图
void BuildDFTImage(IplImage *fourier, IplImage *dst)
{
IplImage *image_Re = 0, *image_Im = 0;
int dft_H, dft_W;
dft_H = fourier->height;
dft_W = fourier->width;
CvMat *dst_Re = cvCreateMat(dft_H,dft_W, CV_64FC1);// double Re, Im;
CvMat *dst_Im = cvCreateMat(dft_H,dft_W, CV_64FC1);//Imaginary part
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2] = tempD;
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2 + 1] = 0.0;
cvConvert(src, src_Re);
cvZero(src_Im);
cvMerge(src_Re, src_Im, 0, 0, sum_src);
cvDFT(sum_src,sum_dst,CV_DXT_FORWARD,0);
cvConvert(sum_dst, fourier);
cvReleaseMat(&src_Re);
//重新安排傅里叶图像中心
// Rearrange the quadrants of Fourier image so that the origin is at
// the image center
double minVal = 0, maxVal = 0;
cvMinMaxLoc( image_Re, &minVal, &maxVal );// Localize minimum and maximum values
// Rearrange the quadrants of Fourier image so that the origin is at
// the image center
int nRow, nCol, i, j, cy, cx;
uchar tmp13, tmp24;
nRow = fourier->height;
具体内容：利用cvDFT函数实现DFT，在频域上利用理想高通和低通滤波器进行滤波，并把滤波过后的图像显示在屏幕上（观察振铃现象），要求截止频率可输入。
3、利用布特沃斯高通和低通滤波器对灰度图像进行频域滤波。
具体内容：利用cvDFT函数实现DFT，在频域上进行利用布特沃斯高通和低通滤波器进行滤波，并把滤波过后的图像显示在屏幕上（观察振铃现象），要求截止频率和n可输入。
CvScalar min;
min.val[0] = minVal;
double scale = 255 / (maxVal - minVal);
cvSubS(image_Re, min, image_Re);
cvConvertScale(image_Re, dst, scale);
cvReleaseImage(&image_Re);
case 1:
tempD = (double)sqrt(1.0*(height - i) * (height - i) + j * j);break;
case 2:
tempD = (double)sqrt(1.0*i * i + (width - j) * (width - j));break;
case 3:
tempD = (double)sqrt(1.0*(height - i) * (height - i) + (width - j) * (width - j));break;
default:
break;
}
switch(FLAG){
case IDEAL_LOW:
if(tempD <= D0){
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2] = 1.0;
nCol = fourier->width;
cy = nRow/2; // image center
cx = nCol/2;
for( j = 0; j < cy; j++ )
{
for( i = 0; i < cx; i++ )
{
tmp13 = CV_IMAGE_ELEM( dst, uchar, j, i);
CvMat *sum_src =cvCreateMat(dft_H,dft_W, CV_64FC2);//2 channels (src_Re, src_Im)
CvMat *sum_dst =cvCreateMat(dft_H,dft_W, CV_64FC2);//2 channels (dst_Re, dst_Im)
image_Re = cvCreateImage(cvGetSize(fourier), IPL_DEPTH_64F, 1);
image_Im = cvCreateImage(cvGetSize(fourier), IPL_DEPTH_64F, 1);//Imaginary part
cvSplit(fourier, image_Re, image_Im, 0, 0 );
cvPow( image_Re, image_Re, 0.5 );
cvReleaseImage(&image_Im);
cvAddS(image_Re, cvScalar(1.0), image_Re); // 1 + Mag
cvLog(image_Re, image_Re ); // log(1 + Mag)
实验名称
实验5：频域滤波
实验目的
掌握图像进行频域滤波的方法和步骤。
1、掌握图像频域DFT变换和反变换的方法。
2、掌握图像频域滤波的步骤
实验内容
1、灰度图像的DFT和IDFT。
具体内容：利用OpenCV提供的cvDFT函数对图像进行DFT和IDFT变换
2、利用理想高通和低通滤波器对灰度图像进行频域滤波
实验完成情况
1、实验步骤：利用OpenCV提供的cvDFT函数对图像进行DFT和IDFT变换
核心代码如下：
//DFT变换
IplImage *DFT(IplImage * src)
{
IplImage* fourier = cvCreateImage(cvGetSize(src),IPL_DEPTH_64F,2);
CV_IMAGE_ELEM( dst, uchar, j, i+cx) =CV_IMAGE_ELEM( dst, uchar, j+cy, i);
CV_IMAGE_ELEM( dst, uchar, j+cy, i) = tmp24;
}
}
}
实验结果如图：
2、实验步骤：利用cvDFT函数实现DFT，在频域上利用理想高通和低通滤波器进行滤波，并把滤波过后的图像显示在屏幕上（观察振铃现象），截止频率可输入。
height = fourier->height;
long x, y;
x = width / 2;
y = height / 2;
CvMat* H_mat;
H_mat = cvCreateMat(fourier->height,fourier->width, CV_64FC2);
for(i = 0; i < height; i++){
核心代码如下：
void PassFilter(IplImage * fourier, int FLAG, double d0, int n1)
{
int i, j;
int state = -1;
double tempD;
long width, height;
width = fourier->width;