如何补偿打印页面照片中的不均匀照明？

import cv2

def histogram_equalization(image):
    # 将图像转换为灰度图像
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # 应用直方图均衡化
    equalized_image = cv2.equalizeHist(gray_image)
    return equalized_image

# 读取图像
image = cv2.imread('path_to_image.jpg')
# 应用直方图均衡化
equalized_image = histogram_equalization(image)
# 显示结果
cv2.imshow('Equalized Image', equalized_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

def adaptive_histogram_equalization(image):
    # 将图像转换为灰度图像
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # 创建CLAHE对象
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
    # 应用自适应直方图均衡化
    equalized_image = clahe.apply(gray_image)
    return equalized_image

# 读取图像
image = cv2.imread('path_to_image.jpg')
# 应用自适应直方图均衡化
equalized_image = adaptive_histogram_equalization(image)
# 显示结果
cv2.imshow('Adaptive Equalized Image', equalized_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import tensorflow as tf
from tensorflow.keras.models import load_model

def deep_learning_correction(image):
    # 加载预训练的深度学习模型
    model = load_model('path_to_model.h5')
    # 预处理图像
    processed_image = preprocess_image(image)
    # 应用模型进行预测
    corrected_image = model.predict(processed_image)
    # 后处理图像
    corrected_image = postprocess_image(corrected_image)
    return corrected_image

# 读取图像
image = cv2.imread('path_to_image.jpg')
# 应用深度学习模型进行补偿
corrected_image = deep_learning_correction(image)
# 显示结果
cv2.imshow('Corrected Image', corrected_image)
cv2.waitKey(0)
cv2.destroyAllWindows()