对一幅带有噪声图像分别采用2种以上空域和频域的滤波方法实现平滑处理，空域和频域至少选择一种方法自己编写代码实现

以下是Python代码实现：

空域滤波：

import cv2
import numpy as np

# 读取图像并加噪声
img = cv2.imread('lena.jpg', 0)
noise = np.random.normal(0, 50, img.shape)
noisy_img = np.clip(img + noise, 0, 255).astype(np.uint8)

# 定义均值滤波器
def mean_filter(img, kernel_size):
    kernel = np.ones((kernel_size, kernel_size), np.float32) / (kernel_size ** 2)
    filtered_img = cv2.filter2D(img, -1, kernel)
    return filtered_img

# 定义中值滤波器
def median_filter(img, kernel_size):
    filtered_img = cv2.medianBlur(img, kernel_size)
    return filtered_img

# 使用均值滤波器和中值滤波器进行平滑处理
mean_filtered_img = mean_filter(noisy_img, 5)
median_filtered_img = median_filter(noisy_img, 5)

# 显示结果
cv2.imshow('Noisy Image', noisy_img)
cv2.imshow('Mean Filtered Image', mean_filtered_img)
cv2.imshow('Median Filtered Image', median_filtered_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

频域滤波：

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

# 读取图像并加噪声
img = cv2.imread('lena.jpg', 0)
noise = np.random.normal(0, 50, img.shape)
noisy_img = np.clip(img + noise, 0, 255).astype(np.uint8)

# 定义傅里叶变换函数
def fft(img):
    f = np.fft.fft2(img)
    fshift = np.fft.fftshift(f)
    return fshift

# 定义傅里叶逆变换函数
def ifft(fshift):
    f_ishift = np.fft.ifftshift(fshift)
    img_back = np.fft.ifft2(f_ishift)
    img_back = np.real(img_back)
    return img_back

# 定义理想低通滤波器
def ideal_lpf(img, d):
    rows, cols = img.shape
    crow, ccol = int(rows / 2), int(cols / 2)
    mask = np.zeros((rows, cols), np.uint8)
    cv2.circle(mask, (ccol, crow), d, 1, -1)
    fshift = fft(img)
    fshift = fshift * mask
    img_back = ifft(fshift)
    img_back = np.clip(img_back, 0, 255).astype(np.uint8)
    return img_back

# 定义高斯低通滤波器
def gaussian_lpf(img, d):
    rows, cols = img.shape
    crow, ccol = int(rows / 2), int(cols / 2)
    x = np.arange(cols)
    y = np.arange(rows)
    x, y = np.meshgrid(x, y)
    mask = np.exp(-((x - ccol) ** 2 + (y - crow) ** 2) / (2 * d ** 2))
    fshift = fft(img)
    fshift = fshift * mask
    img_back = ifft(fshift)
    img_back = np.clip(img_back, 0, 255).astype(np.uint8)
    return img_back

# 使用理想低通滤波器和高斯低通滤波器进行平滑处理
ideal_lpf_img = ideal_lpf(noisy_img, 50)
gaussian_lpf_img = gaussian_lpf(noisy_img, 50)

# 显示结果
plt.subplot(2, 2, 1), plt.imshow(noisy_img, cmap='gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(2, 2, 2), plt.imshow(ideal_lpf_img, cmap='gray')
plt.title('Ideal LPF Image'), plt.xticks([]), plt.yticks([])
plt.subplot(2, 2, 3), plt.imshow(gaussian_lpf_img, cmap='gray')
plt.title('Gaussian LPF Image'), plt.xticks([]), plt.yticks([])
plt.show()