基于暗通道先验的图像去雾算法解析与实现

基于暗通道先验的图像去雾算法解析与实现

引用

CSDN

1.

https://blog.csdn.net/hhhhhhhhhhwwwwwwwwww/article/details/145707812

图像去雾是计算机视觉领域的重要研究方向之一，广泛应用于摄影、遥感、自动驾驶等领域。基于暗通道先验的图像去雾算法是目前最常用的算法之一，其核心思想是利用无雾图像中局部区域至少存在一个颜色通道的像素值趋近于零的特性来恢复清晰图像。本文将详细介绍该算法的原理和实现步骤。

一、算法背景

何凯明团队于2009年提出的暗通道先验去雾算法《single image haze removal using dark channel prior》，通过统计发现：在无雾图像的局部区域中，至少存在一个颜色通道的像素值趋近于零。这一发现为图像去雾提供了重要的理论依据，其数学模型可表示为：

I(x) = J(x)t(x) + A(1 - t(x))

其中：

• I(x)：观测到的有雾图像
• J(x)：待恢复的无雾图像
• t(x)：透射率
• A：全局大气光值

二、算法原理

1. 暗通道计算

通过取RGB三通道最小值并进行形态学腐蚀操作：

def dark_channel(img, size=15):
    r, g, b = cv2.split(img)
    min_img = cv2.min(r, cv2.min(g, b))
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (size, size))
    return cv2.erode(min_img, kernel)

2. 大气光估计

选取图像中最亮像素的0.1%作为大气光值：

def get_atmo(img, percent=0.001):
    mean_perpix = np.mean(img, axis=2).reshape(-1)
    mean_topper = mean_perpix[:int(img.shape[0] * img.shape[1] * percent)]
    return np.mean(mean_topper)

3. 透射率估计

t(x) = 1 - ω ⋅ dark_channel(I/A)

def get_trans(img, atom, w=0.95):
    x = img / atom
    return 1 - w * dark_channel(x, 7)

4. 引导滤波优化

使用灰度图作为引导图像进行透射率优化：

# 引导滤波
def guided_filter(p, i, r, e):
    """
    :param p: input image
    :param i: guidance image
    :param r: radius
    :param e: regularization
    :return: filtering output q
    """
    # 1
    mean_I = cv2.boxFilter(i, cv2.CV_64F, (r, r))
    mean_p = cv2.boxFilter(p, cv2.CV_64F, (r, r))
    corr_I = cv2.boxFilter(i * i, cv2.CV_64F, (r, r))
    corr_Ip = cv2.boxFilter(i * p, cv2.CV_64F, (r, r))
    # 2
    var_I = corr_I - mean_I * mean_I
    cov_Ip = corr_Ip - mean_I * mean_p
    # 3
    a = cov_Ip / (var_I + e)
    b = mean_p - a * mean_I
    # 4
    mean_a = cv2.boxFilter(a, cv2.CV_64F, (r, r))
    mean_b = cv2.boxFilter(b, cv2.CV_64F, (r, r))
    # 5
    q = mean_a * i + mean_b
    return q

三、完整实现代码

import cv2
import numpy as np
import os

# 计算雾化图像的暗通道
def dark_channel(img, size=15):
    r, g, b = cv2.split(img)
    min_img = cv2.min(r, cv2.min(g, b))  # 取最暗通道
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (size, size))
    dc_img = cv2.erode(min_img, kernel)
    return dc_img

# 估计全局大气光值
def get_atmo(img, percent=0.001):
    mean_perpix = np.mean(img, axis=2).reshape(-1)
    mean_topper = mean_perpix[:int(img.shape[0] * img.shape[1] * percent)]
    return np.mean(mean_topper)

# 估算透射率图
def get_trans(img, atom, w=0.95):
    x = img / atom
    t = 1 - w * dark_channel(x, 15)
    return t

# 引导滤波
def guided_filter(p, i, r, e):
    """
    :param p: input image
    :param i: guidance image
    :param r: radius
    :param e: regularization
    :return: filtering output q
    """
    # 1
    mean_I = cv2.boxFilter(i, cv2.CV_64F, (r, r))
    mean_p = cv2.boxFilter(p, cv2.CV_64F, (r, r))
    corr_I = cv2.boxFilter(i * i, cv2.CV_64F, (r, r))
    corr_Ip = cv2.boxFilter(i * p, cv2.CV_64F, (r, r))
    # 2
    var_I = corr_I - mean_I * mean_I
    cov_Ip = corr_Ip - mean_I * mean_p
    # 3
    a = cov_Ip / (var_I + e)
    b = mean_p - a * mean_I
    # 4
    mean_a = cv2.boxFilter(a, cv2.CV_64F, (r, r))
    mean_b = cv2.boxFilter(b, cv2.CV_64F, (r, r))
    # 5
    q = mean_a * i + mean_b
    return q

def dehaze(im):
    img = im.astype('float64') / 255
    img_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY).astype('float64') / 255
    atom = get_atmo(img)
    trans = get_trans(img, atom)
    trans_guided = guided_filter(trans, img_gray, 20, 0.0001)
    trans_guided = cv2.max(trans_guided, 0.25)
    result = np.empty_like(img)
    for i in range(3):
        result[:, :, i] = (img[:, :, i] - atom) / trans_guided + atom
    return result * 255

if __name__ == '__main__':
    image_path= 'images/img.png'
    im = cv2.imread(image_path)
    img = im.astype('float64') / 255
    img_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY).astype('float64') / 255
    atom = get_atmo(img)
    trans = get_trans(img, atom)
    trans_guided = guided_filter(trans, img_gray, 10, 0.0001)
    trans_guided = cv2.max(trans_guided, 0.25)
    result = np.empty_like(img)
    for i in range(3):
        result[:, :, i] = (img[:, :, i] - atom) / trans_guided + atom
    cv2.imwrite('images/img.png', result * 255)