傅里叶变换图像去噪与中值滤波去噪代码

傅里叶变换图像去噪代码：

import cv2
import numpy as np
from matplotlib import pyplot as plt

# 读取图片
img = cv2.imread('image.jpg', 0)

# 傅里叶变换
dft = cv2.dft(np.float32(img), flags=cv2.DFT_COMPLEX_OUTPUT)
dft_shift = np.fft.fftshift(dft)

# 高通滤波器
rows, cols = img.shape
crow, ccol = rows//2, cols//2
mask = np.ones((rows, cols, 2), np.uint8)
r = 60
mask[crow-r:crow+r, ccol-r:ccol+r] = 0

# 傅里叶反变换
fshift = dft_shift*mask
f_ishift = np.fft.ifftshift(fshift)
img_back = cv2.idft(f_ishift)
img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])

# 显示原图和处理后的图
plt.subplot(121), plt.imshow(img, cmap='gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(img_back, cmap='gray')
plt.title('After FFT'), plt.xticks([]), plt.yticks([])
plt.show()

中值滤波去噪代码：

import cv2
import numpy as np
from matplotlib import pyplot as plt

# 读取图片
img = cv2.imread('image.jpg', 0)

# 中值滤波
img_median = cv2.medianBlur(img, 5)

# 显示原图和处理后的图
plt.subplot(121), plt.imshow(img, cmap='gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(img_median, cmap='gray')
plt.title('After Median Filtering'), plt.xticks([]), plt.yticks([])
plt.show()
``