I need to apply a high pass filter to an image. The approach I'm following uses Fourier transform to apply a circular filter which would eliminate low frequencies.
Say I have a frequency threshold below which frequencies should be filtered out, i.e. cut-off frequency.
The resulting spectogram shape of the FTT operation is the same than the image, hence I am not sure how to link the radius value of the mask to the cut-off frequency of interest.
# read image image = cv2.imread('path/image_name.jpg', 0) img = np.asarray(image) # FFT dft = cv2.dft(np.float32(img), flags=cv2.DFT_COMPLEX_OUTPUT) dft_shift = np.fft.fftshift(dft) magnitude_spectrum = 20 * np.log(cv2.magnitude(dft_shift[:,:,0], dft_shift[:,:,1])) # center of image rows, cols = img.shape crow, ccol = int(rows/2), int(cols/2) # create mask - circular filter mask = np.ones((rows, cols, 2), np.uint8) r=4.34 center = [crow, ccol] x, y = np.ogrid[:rows, :cols] mask_area = (x - center)**2 + (y - center)**2 <= r*r mask[mask_area] = 0 # apply filter fshift = dft_shift * mask # return to spatial domain f_ishift = np.fft.ifftshift(fshift) img_back = cv2.idft(f_ishift) img_back = cv2.magnitude(img_back[:,:,0], img_back[:,:,1])
What is the intuition for the radius choice for the mask? I understand that a higher the radius will filter out more frequencies. However, how would I know that frequencies above the threshold are not being affected by the filter?