Image restoration algorithm based on compensated transmission and adaptive haze concentration coefficient

Yan YANG; Zhiwei WANG

doi:10.11959/j.issn.1000-436x.2020009

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Image restoration algorithm based on compensated transmission and adaptive haze concentration coefficient

Papers | 更新时间：2024-06-05

- Image restoration algorithm based on compensated transmission and adaptive haze concentration coefficient
- Journal on Communications Vol. 41, Issue 1, Pages: 66-75(2020)
- 作者机构：
  
  兰州交通大学电子与信息工程学院，甘肃兰州 730070
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61561030);Fundamental Research Funds for the Gansu Provincial Finance Department(214138);Research Fund of Teaching Reform Project of Lanzhou Jiaotong University(160012)
- DOI：10.11959/j.issn.1000-436x.2020009
  CLC： TP391.41
- Online First：2020-01，
  
  Published：25 January 2020
- 稿件说明：
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Yan YANG, Zhiwei WANG. Image restoration algorithm based on compensated transmission and adaptive haze concentration coefficient[J]. Journal on Communications, 2020, 41(1): 66-75.
DOI：

Yan YANG, Zhiwei WANG. Image restoration algorithm based on compensated transmission and adaptive haze concentration coefficient[J]. Journal on Communications, 2020, 41(1): 66-75. DOI： 10.11959/j.issn.1000-436x.2020009.

摘要

针对传统暗通道先验易在高亮度区域失真和产生光晕效应的不足，提出一种基于补偿透射率和自适应雾浓度系数的雾天图像复原算法。首先利用高斯函数拟合有雾和无雾图像间的衰减关系，通过修正透射率对高亮区域进行补偿。然后分析雾气特性，提出亮度熵概念，对原图亮通道进行逐像素处理求取熵值，结合高斯金字塔提取纹理特征，得到雾气分布图；同时建立一种线性变换来自适应求取雾浓度系数，并获得优化透射率。最后改进局部大气光的获取方法，结合大气散射模型得到复原结果。实验表明，所提算法可以有效复原出降质图像的颜色与细节，明亮度适宜，去雾程度彻底，效果清晰自然。

Abstract

Aiming at the drawbacks of traditional dark channel prior

which was prone to distortion and Halo effects in the bright areas

a haze image restoration algorithm based on compensated transmission and adaptive haze concentration coefficient was proposed.First of all

a Gaussian function was used to fit the attenuation relationship between the haze and haze-free image

and the compensation transmission was set to correct the initial transmission.Then the characteristics of haze was analyzed

the concept of brightness entropy was introduced and the bright channel operation was performed to acquire entropy value with pixel by pixel.Combined with the Gaussian pyramid to extract texture features

the haze distribution map was obtained.An adaptive transformation was established to seek the haze concentration coefficient and get the accurate transmission.Finally

the recovery results were restored by improved atmospheric light value and atmospheric scattering model.Experimental results show that the recovered image has better color and detail

the degree of dehazing is thorough

the brightness is appropriate

and the effect is clear and natural.

关键词

Keywords

references

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