图像处理 使用python和opencv进行示例 对图像进行小波变换 要求如下： ①Perform wavelet forward transform on the image, set all wavelet coefficients outside the wavelet domain image subband to 0, and then perform wavelet inverse transform to extract the low-frequency background portion L of the image ②Use the original image to remove the background part of the image to get the high-frequency texture part H of the image. ③Use Criminisi algorithm to repair texture edges. ④Resuming the low-frequency portion L and the repaired high-frequency portion H+ to obtain an enhanced image.

下面是一个使用Python和OpenCV进行小波变换的示例代码：

import cv2
import numpy as np

# 读取图像
img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# 定义小波变换函数
def wavelet_transform(img, level=3):
    # 小波系数
    coeffs = pywt.wavedec2(img, 'haar', level=level)
    # 将所有小波系数设为0，获取低频分量
    coeffs_arr = []
    for i, coeff in enumerate(coeffs):
        if i == 0:
            coeffs_arr.append(coeff)
        else:
            coeffs_arr.append((np.zeros_like(coeff), np.zeros_like(coeff), np.zeros_like(coeff)))
    # 小波反变换
    img_low = pywt.waverec2(coeffs_arr, 'haar')
    # 获取高频分量
    img_high = img - img_low
    return img_low, img_high

# 调用小波变换函数
img_low, img_high = wavelet_transform(img)

# 使用Criminisi算法进行边缘修复
# ...

# 合并低频分量和修复后的高频分量，得到增强后的图像
img_enhanced = img_low + img_high

# 显示增强后的图像
cv2.imshow('Enhanced Image', img_enhanced)
cv2.waitKey(0)
cv2.destroyAllWindows()

需要注意的是，这里的边缘修复部分需要使用Criminisi算法或其他合适的算法来实现。具体的实现方法可能因为应用场景而异，需要自行探索和实现。