Single image dehazing method based on improved atmospheric scattering model

被引：0

作者：

Yang Y. ^{[1
]}

Qiu G. ^{[1
]}

Huang S. ^{[2
]}

Wan W. ^{[3
]}

Hu W. ^{[1
]}

机构：

[1] School of Information Technology, Jiangxi University of Finance and Economics, Nanchang

[2] School of Software, Tiangong University, Tianjin

[3] School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2022年 / 48卷 / 08期

基金：

中国国家自然科学基金;

关键词：

adaptive weight; atmospheric scattering model; dark channel prior; image dehazing; objective function;

D O I：

10.13700/j.bh.1001-5965.2021.0532

中图分类号：

学科分类号：

摘要：

Images obtained in foggy conditions often suffer from low contrast, color loss, and noise. At present, many traditional dehazing methods mainly focus on solving problems such as low contrast and color loss, but do not consider the hidden noise light scattered by dust particles in the air, resulting in a large amount of noise in the dehazing results. This work provides an image dehazing algorithm based on an enhanced atmospheric scattering model to address the mentioned problems. Firstly, according to the characteristics of haze, the traditional atmospheric scattering model of hazy imaging is improved by adding the noise light reflected by the medium in the air. Then, in order to address the transmission calculation inaccuracy problem for the dark channel prior, a refined calculation method of transmission is constructed according to the improved model. Finally, combined with the idea of edge preservation and noise suppression of the total variation model, a new objective function is constructed and solved iteratively to obtain the final defogging image. A large number of experimental results and comparative analyses show that the proposed method can effectively remove the haze in the image, reduce the noise in the dehazing results, and retain the rich texture information in the image. © 2022 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.

引用

页码：1364 / 1375

页数：11

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