基于自适应gamma校正估计的图像去雾算法

吴正平; 岑帅红

doi:10.37188/CJLCD.2021-0182

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基于自适应gamma校正估计的图像去雾算法

图像处理 | 更新时间：2022-01-19

- 基于自适应gamma校正估计的图像去雾算法
- Image dehazing algorithm based on adaptive gamma correction estimation
- 液晶与显示 2022年37卷第1期页码：106-115
- 作者机构：
  
  三峡大学电气与新能源学院, 湖北宜昌 201306
- 作者简介：
  
  [ "吴正平(1966-), 男, 湖南宁乡人, 博士, 教授, 2004年于华中科技大学获得博士学位, 主要从事数字图像处理、复杂网络、检测技术与自动化装置方面的研究。E-mail: wuzp@ctgu.edu.cn" ]
  [ "岑帅红(1992-), 男, 河南周口人, 硕士研究生, 2019年于河南科技学院获得学士学位, 主要从事数字图像处理方面的研究。E-mail: 1002473401@qq.com" ]
- 基金信息：
  
  国家自然科学基金(61871258)
- DOI：10.37188/CJLCD.2021-0182
  中图分类号： TP391.41
- 收稿日期：2021-07-09，
  
  修回日期：2021-08-31，
  
  纸质出版日期：2022-01
- 稿件说明：
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吴正平, 岑帅红. 基于自适应gamma校正估计的图像去雾算法[J]. 液晶与显示, 2022,37(1):106-115.

Zheng-ping WU, Shuai-hong CEN. Image dehazing algorithm based on adaptive gamma correction estimation[J]. Chinese journal of liquid crystals and displays, 2022, 37(1): 106-115.
吴正平, 岑帅红. 基于自适应gamma校正估计的图像去雾算法[J]. 液晶与显示, 2022,37(1):106-115. DOI： 10.37188/CJLCD.2021-0182.

Zheng-ping WU, Shuai-hong CEN. Image dehazing algorithm based on adaptive gamma correction estimation[J]. Chinese journal of liquid crystals and displays, 2022, 37(1): 106-115. DOI： 10.37188/CJLCD.2021-0182.

摘要

针对当前流行的图像去雾算法存在去雾过度造成图像颜色失真，或者去雾不足等问题，提出了一种自适应gamma校正估计的图像去雾算法。首先根据图像亮度，利用不同的gamma校正函数拟合不同场景深度下有雾图像与无雾图像之间的关系，自适应估算出无雾图像最小通道，并通过引导滤波算法进行修正，保持局部区域内线性的关系，进而根据大气散射模型得到初始透射率，然后通过高斯相对性进行优化；另外，通过增加搜索领域，将有雾图像的蓝色通道上半部分作为输入对四叉树算法进行改进，得到场景最深处所对应的有雾图像像素值作为大气光值；最后通过gamma校正函数对复原图像的亮度进行增强。实验结果表明复原图像的对比度、平均梯度分别平均提高了40.66%，20.98%，并具有较高的信息熵。上述算法去雾显著，复原图像具有较高的清晰度。

Abstract

Aiming at the problems of the current popular image dehazing algorithms

such as excessive dehazing which causes image color distortion or insufficient dehazing

an image dehazing algorithm with adaptive gamma correction estimation is proposed. Firstly

according to the brightness of the image

the different gamma correction functions are used to fit the relationship between the hazy image and the fogless image under different scene depths

and the minimum channel of fogless image is adaptively estimated. Then

the minimum channel of the non-fog image is corrected by the guided filtering algorithm to maintain the linear relationship between the hazy image and the non-fog image in the local area with the same depth of the scene. The initial transmittance according to the atmospheric scattering model is obtained. Then

the initial transmittance through Gaussian relativity is optimized to obtain the precise transmittance with structure retention and local smoothness. By increasing the search field

the upper half of the blue channel of the hazy image is used as input to improve the quadtree algorithm

and stably obtain the hazy image pixel value corresponding to the deepest part of the scene as the atmospheric light value. Finally

the brightness of the restored image is enhanced by the gamma correction function. The experimental results show that the contrast and average gradient of the restored image are increased respectively by 40.66% and 20.98% on average

and have higher information entropy. The above algorithm dehazes significantly

and the restored image has higher definition

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Keywords

references

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