根据水下光传播路径和0度45度90度135度 偏振图片进行水下图像增强 python程序

下面是一个简单的 Python 程序，用于根据水下光传播路径和偏振图像进行水下图像增强：

import cv2
import numpy as np

# 读取水下图像和偏振图像
underwater_image = cv2.imread('underwater_image.png')
polarized_image_0 = cv2.imread('polarized_image_0.png')
polarized_image_45 = cv2.imread('polarized_image_45.png')
polarized_image_90 = cv2.imread('polarized_image_90.png')
polarized_image_135 = cv2.imread('polarized_image_135.png')

# 定义函数，根据偏振图像和水下光传播路径进行增强
def enhance_underwater_image(underwater_image, polarized_image_0, polarized_image_45, polarized_image_90, polarized_image_135):
    # 将偏振图像转换为灰度图像
    polarized_image_0_gray = cv2.cvtColor(polarized_image_0, cv2.COLOR_BGR2GRAY)
    polarized_image_45_gray = cv2.cvtColor(polarized_image_45, cv2.COLOR_BGR2GRAY)
    polarized_image_90_gray = cv2.cvtColor(polarized_image_90, cv2.COLOR_BGR2GRAY)
    polarized_image_135_gray = cv2.cvtColor(polarized_image_135, cv2.COLOR_BGR2GRAY)

    # 计算水下图像中的散射光和直射光
    scattered_light = (polarized_image_0_gray + polarized_image_90_gray) / 2.0
    direct_light = (polarized_image_45_gray + polarized_image_135_gray) / 2.0

    # 计算散射光和直射光之间的差异
    diff = scattered_light - direct_light

    # 对水下图像进行增强
    enhanced_image = underwater_image + np.uint8(diff)

    return enhanced_image

# 调用函数，进行水下图像增强
enhanced_image = enhance_underwater_image(underwater_image, polarized_image_0, polarized_image_45, polarized_image_90, polarized_image_135)

# 显示结果
cv2.imshow('Underwater Image', underwater_image)
cv2.imshow('Enhanced Image', enhanced_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

在这个程序中，我们使用 OpenCV 库来读取水下图像和偏振图像，并定义了一个函数 `enhance_underwater_image` 来对水下图像进行增强。在函数中，我们首先将偏振图像转换为灰度图像，并计算出水下图像中的散射光和直射光。然后，我们通过计算散射光和直射光之间的差异来对水下图像进行增强。最后，我们调用函数并显示结果。

需要注意的是，这个程序只是一个简单的示例，实际应用中需要根据具体情况进行调整和优化。