用opencv 实现一个MSR图像增强算法

MSR（Multi-Scale Retinex）是一种图像增强算法，主要用于提高图像的对比度和亮度。下面是用OpenCV实现MSR算法的步骤：

1. 加载图像并将其转换为灰度图像。

import cv2

img = cv2.imread('input.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

2. 对灰度图像进行高斯模糊。

sigma_list = [15, 80, 250]
retinex = np.zeros_like(gray.astype(np.float32))
for sigma in sigma_list:
    retinex += np.log10(gray) - np.log10(cv2.GaussianBlur(gray, (0, 0), sigma))
retinex = retinex / len(sigma_list) + np.log10(gray)
retinex = np.power(10, retinex)

3. 对高斯模糊后的图像进行差分。

retinex_diff = np.zeros_like(gray.astype(np.float32))
for sigma in sigma_list:
    retinex_diff += np.abs(cv2.GaussianBlur(gray, (0, 0), sigma) - gray)
retinex_diff = retinex_diff / len(sigma_list)

4. 对图像进行归一化处理。

retinex_normalized = cv2.normalize(retinex, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
retinex_diff_normalized = cv2.normalize(retinex_diff, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)

5. 将增强后的图像保存到本地。

cv2.imwrite('output.jpg', retinex_normalized)
cv2.imwrite('diff.jpg', retinex_diff_normalized)

完整代码：

import cv2
import numpy as np

# 加载图像并将其转换为灰度图像
img = cv2.imread('input.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 对灰度图像进行高斯模糊
sigma_list = [15, 80, 250]
retinex = np.zeros_like(gray.astype(np.float32))
for sigma in sigma_list:
    retinex += np.log10(gray) - np.log10(cv2.GaussianBlur(gray, (0, 0), sigma))
retinex = retinex / len(sigma_list) + np.log10(gray)
retinex = np.power(10, retinex)

# 对高斯模糊后的图像进行差分
retinex_diff = np.zeros_like(gray.astype(np.float32))
for sigma in sigma_list:
    retinex_diff += np.abs(cv2.GaussianBlur(gray, (0, 0), sigma) - gray)
retinex_diff = retinex_diff / len(sigma_list)

# 对图像进行归一化处理
retinex_normalized = cv2.normalize(retinex, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
retinex_diff_normalized = cv2.normalize(retinex_diff, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)

# 将增强后的图像保存到本地
cv2.imwrite('output.jpg', retinex_normalized)
cv2.imwrite('diff.jpg', retinex_diff_normalized)

注意：该算法可能会导致图像出现过度增强的情况，需要根据实际情况进行调整。