给出使用小波变换插值法对图片进行超分辨率重建的python代码

以下是使用小波变换插值法进行图片超分辨率重建的Python代码：

import cv2
import numpy as np
import pywt

def upsample(image):
    # Apply wavelet transform
    coeffs = pywt.dwt2(image, 'haar')
    LL, (LH, HL, HH) = coeffs

    # Upsample LH, HL and HH coefficients
    LH_recon = cv2.resize(LH, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
    HL_recon = cv2.resize(HL, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
    HH_recon = cv2.resize(HH, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
    new_coeffs = [LL, (LH_recon, HL_recon, HH_recon)]

    # Apply inverse wavelet transform
    new_image = pywt.idwt2(new_coeffs, 'haar')
    return new_image

# Load input image
image = cv2.imread('input_image.jpg', cv2.IMREAD_GRAYSCALE)
# Upsample input image
new_image = upsample(image)
# Save output image
cv2.imwrite('output_image.jpg', new_image)

希望这个代码能够帮助到你！如果你有其它问题，欢迎继续询问。