通过python实现对彩色图像的频域模糊及复原

可以通过将彩色图像转换为RGB三个通道的灰度图像，然后分别进行频域模糊和复原。

1. 首先，使用OpenCV库将彩色图像读入，并将其转换为灰度图像：

import cv2

img = cv2.imread('color_image.jpg')
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

2. 然后，使用numpy库中的fft2函数将灰度图像转换为频域图像，并进行频域滤波，例如低通滤波器：

import numpy as np

# 2D fast Fourier transform to convert image to frequency domain
f = np.fft.fft2(gray_img)

# Shift the zero-frequency component to the center of the spectrum
fshift = np.fft.fftshift(f)

# Create a low pass filter to remove high frequency components
rows, cols = gray_img.shape
crow, ccol = rows//2, cols//2
mask = np.zeros((rows, cols), np.uint8)
mask[crow-30:crow+30, ccol-30:ccol+30] = 1

# Apply the mask to the frequency domain image
fshift = fshift * mask

# Shift the zero-frequency component back to the corners of the spectrum
f_ishift = np.fft.ifftshift(fshift)

# 2D inverse fast Fourier transform to convert frequency domain image to spatial image
img_back = np.fft.ifft2(f_ishift)

# Convert the real part of the complex image to an 8-bit grayscale image
img_back = np.abs(img_back).astype(np.uint8)

3. 最后，显示频域模糊和复原图像：

# Show the original image, frequency domain blurred image, and recovered image
cv2.imshow('Original Image', img)
cv2.imshow('Frequency Domain Blurred Image', img_back)
cv2.waitKey(0)
cv2.destroyAllWindows()

这样就可以通过Python实现对彩色图像的频域模糊及复原了。