图像处理UDCP算法Python

UDCP（Unified Dark Channel Prior）算法是一种用于图像去雾的算法。它基于暗通道先验假设，通过估计图像中每个像素点的暗通道来提取雾霾信息，进而进行图像去雾处理。

以下是一个使用Python实现UDCP算法的示例代码：

import numpy as np
import cv2

def dark_channel(img, window_size):
    # 计算每个像素点的暗通道
    padded_img = cv2.copyMakeBorder(img, window_size // 2, window_size // 2, window_size // 2, window_size // 2, cv2.BORDER_CONSTANT, value=255)
    dark_ch = np.min(cv2.boxFilter(padded_img, -1, (window_size, window_size)), axis=2)
    return dark_ch

def estimate_atmospheric_light(img, dark_ch):
    # 估计全局大气光
    flat_dark_ch = dark_ch.flatten()
    sorted_indices = np.argsort(flat_dark_ch)
    top_indices = sorted_indices[-int(0.001 * len(sorted_indices)):]
    atmospheric_light = np.max(img.reshape(-1, 3)[top_indices], axis=0)
    return atmospheric_light

def estimate_transmission(img, atmospheric_light, omega=0.95, window_size=15):
    # 估计透射率
    normalized_img = img.astype(np.float32) / atmospheric_light.astype(np.float32)
    dark_ch = dark_channel(normalized_img, window_size)
    transmission = 1 - omega * dark_ch
    return transmission

def dehaze(img, transmission, atmospheric_light, t0=0.1, guided_filter_radius=40, epsilon=0.001):
    # 图像去雾
    transmission = np.maximum(transmission, t0)
    refined_transmission = cv2.ximgproc.guidedFilter(img.astype(np.float32) / 255, transmission.astype(np.float32), guided_filter_radius, epsilon)
    dehazed_img = (img.astype(np.float32) - atmospheric_light.astype(np.float32)) / refined_transmission + atmospheric_light.astype(np.float32)
    dehazed_img = np.clip(dehazed_img, 0, 255).astype(np.uint8)
    return dehazed_img

# 读取输入图像
input_img_path = 'input_image.jpg'
img = cv2.imread(input_img_path)

# 参数设置
window_size = 15
omega = 0.95
t0 = 0.1
guided_filter_radius = 40
epsilon = 0.001

# UDCP算法处理
dark_ch = dark_channel(img, window_size)
atmospheric_light = estimate_atmospheric_light(img, dark_ch)
transmission = estimate_transmission(img, atmospheric_light, omega, window_size)
dehazed_img = dehaze(img, transmission, atmospheric_light, t0, guided_filter_radius, epsilon)

# 显示结果
cv2.imshow('Input Image', img)
cv2.imshow('Dehazed Image', dehazed_img)
cv2.waitKey(0)
cv2.destroyAllWindows()