基于Retinex理论的MSR算法代码

Retinex理论是指色彩还原理论，是一种常用于图像增强的方法。其中，MSR（Multi-scale Retinex with Color Restoration）算法是一种基于Retinex理论的图像增强算法。下面给出基于Retinex理论的MSR算法的Python实现代码。

import cv2
import numpy as np

def MSR(image, sigma_list):
    """
    Multi-scale Retinex with Color Restoration (MSR) algorithm
    :param image: the input image
    :param sigma_list: a list of sigma values for Gaussian blurring
    :return: the enhanced image
    """
    # Convert the input image to the YCrCb color space
    ycrcb = cv2.cvtColor(image, cv2.COLOR_BGR2YCrCb)

    # Split the YCrCb channels
    y, cr, cb = cv2.split(ycrcb)

    # Create an empty output image
    output = np.zeros(image.shape, dtype=np.float32)

    # Apply multi-scale Retinex algorithm to the Y channel
    for sigma in sigma_list:
        # Blur the image using a Gaussian filter
        blur = cv2.GaussianBlur(y, (0, 0), sigma)

        # Calculate the log of the blurred image
        log = np.log(image + 1) - np.log(blur + 1)

        # Add the log values to the output image
        output += log

    # Scale the output image
    output = output / len(sigma_list)

    # Apply color restoration to the output image
    output = color_restoration(output, cr, cb)

    # Convert the output image to the BGR color space
    output = cv2.cvtColor(output, cv2.COLOR_YCrCb2BGR)

    # Clip the pixel values to the range [0, 255]
    output = np.clip(output, 0, 255)

    # Convert the pixel values to the uint8 data type
    output = np.uint8(output)

    return output


def color_restoration(image, cr, cb):
    """
    Color restoration function
    :param image: the input image
    :param cr: the Cr channel of the input image
    :param cb: the Cb channel of the input image
    :return: the output image with restored color
    """
    # Calculate the mean of the input image
    mean = np.mean(image)

    # Create an empty output image
    output = np.zeros(image.shape, dtype=np.float32)

    # Apply color restoration algorithm to the image
    for i in range(image.shape[0]):
        for j in range(image.shape[1]):
            # Calculate the chrominance values
            chroma = np.sqrt((cr[i, j] ** 2) + (cb[i, j] ** 2))

            # Calculate the color restoration factor
            factor = np.log10(1 + mean / chroma)

            # Apply the color restoration factor to the image
            output[i, j] = image[i, j] * factor

    return output

在上面的代码中，MSR函数是实现了多尺度Retinex算法的函数。该函数将输入图像转换到YCrCb颜色空间，并将其分解为Y，Cr和Cb通道。然后，它使用一个高斯滤波器对Y通道进行多尺度处理，并将所有处理后的图像叠加起来，得到增强后的Y通道。最后，它使用color_restoration函数对Y通道进行颜色恢复，并将输出图像转换回BGR颜色空间。

color_restoration函数是一个用于颜色恢复的辅助函数。该函数通过计算Cr和Cb通道的平均值和Y通道的色度值来计算颜色恢复系数，并将其应用于Y通道。最后，该函数返回颜色恢复后的图像。

使用MSR算法增强图像的示例代码如下：

# Load the input image
image = cv2.imread('input.jpg')

# Apply the MSR algorithm to the image
sigma_list = [15, 80, 250]
output = MSR(image, sigma_list)

# Display the input and output images
cv2.imshow('Input', image)
cv2.imshow('Output', output)
cv2.waitKey(0)

在上面的代码中，我们加载了一个名为input.jpg的图像，并将其传递给MSR函数，使用sigma_list参数指定了高斯滤波器的尺度。最后，我们显示了输入图像和增强后的输出图像。