Haze degree estimation problem in image defogging technology

The problem of haze degree estimation in image dehazing technology

Introduction
With the acceleration of urbanization, the problem of air pollution is becoming increasingly serious, and haze has become a A common phenomenon in urban life. Among them, haze brings challenges to visual tasks such as image acquisition and image processing. In order to improve the image quality degradation problem caused by haze, researchers have proposed various image defogging algorithms. Among these algorithms, accurately estimating the degree of haze in the image is crucial to improving the dehazing effect. This article will discuss the haze degree estimation problem in image dehazing technology and provide specific code examples.

1. The importance of haze degree estimation
Haze degree estimation is an important part of the image dehazing task. By accurately estimating the degree of haze in an image, it can help the dehazing algorithm better understand the mixed haze and scene information in the image, thereby achieving a more accurate dehazing effect. In practical applications, it is often necessary to select appropriate dehazing algorithms and parameters based on the haze level of the image, thereby improving the effect of image processing.

2. Commonly used haze degree estimation methods

1. The haze degree estimation method based on single-scale dark channel prior:
   The single-scale dark channel prior is based on analysis Dark channel in outdoor images to estimate the extent of haze. This method assumes that the minimum value of the R, G, and B channels of a certain pixel in the image (non-light source point) corresponds to one of the brightest pixels in the image, and estimates the haze level through the depth information of the brightest pixel. degree. The specific calculation formula is:
   A = min(R, G, B)
   t(x) = 1 - w * min(R/G, R/B, R/A)
   where, R , G, and B respectively represent the intensity values ​​of the red, green, and blue channels at the pixel point (x, y), A represents the depth value of the brightest pixel in the image, and w is a fixed weight.
2. Haze degree estimation method based on image contrast:
   This method estimates the degree of haze based on the contrast of the image. Typically, haze images have low contrast, while non-haze images have high contrast. Therefore, the degree of haze can be estimated by comparing the contrast difference between the original image and the dehazed image. A simple calculation method is to calculate the grayscale histogram of the image and calculate the mean square error of the histogram.

3. Code Example
The following is a code example of haze level estimation based on single-scale dark channel prior using Python language:

import cv2
import numpy as np

def estimate_haze_level(image):
    # 计算每个像素点的最小通道值
    min_channel = np.min(image, axis=2)
    
    # 计算最亮像素点的深度值
    A = np.max(min_channel)
    
    # 根据公式计算雾霾程度
    haze_level = 1 - 0.95 * (min_channel / A)
    
    return haze_level

# 读取原始图像
image = cv2.imread("input.jpg")

# 估计雾霾程度
haze_level = estimate_haze_level(image)

# 输出雾霾程度
print("Haze level:", haze_level)

4. Summary
The haze degree estimation problem in image dehazing technology is crucial to improving the dehazing effect. This article introduces the importance of haze level estimation and provides a code example for haze level estimation based on a single-scale dark channel prior. By rationally using image dehazing algorithms and haze degree estimation methods, the problem of image quality degradation caused by haze can be effectively improved and the accuracy and effect of image processing can be improved. As research continues to deepen, it is believed that image defogging technology will be more widely used in the future.

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