结合景深估计的高斯衰减与自适应补偿去雾

杨燕; 张国强; 姜沛沛

doi:10.3788/OPE.20192711.2439

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结合景深估计的高斯衰减与自适应补偿去雾

信息科学 | 更新时间：2020-08-13

- 结合景深估计的高斯衰减与自适应补偿去雾
- Gaussian decay and adaptive compensation dehazing algorithm combined with scene depth estimation
- 光学精密工程 2019年27卷第11期页码：2439-2449
- 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：
  
  [ "杨燕(1972-)，女，河南临颍人，博士，教授、硕士生导师，1995年于兰州铁道学院获得学士学位，2006年于兰州交通大学获得硕士学位，2010年于兰州大学获得博士学位，主要从事数字图像处理、智能信息处理及语音信号处理方面的研究。E-mail:yangyantd@mail.lzjtu.cn" ]
  [ "张国强(1992-)，男，陕西武功人，硕士研究生，主要研究方向为数字图像处理。E-mail: 791783286@qq.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61561030);兰州交通大学教改项目资助(160012)
- DOI：10.3788/OPE.20192711.2439
  中图分类号： TP391
- 收稿日期：2019-05-23，
  
  录用日期：2019-7-19，
  
  纸质出版日期：2019-11-15
- 稿件说明：
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杨燕, 张国强, 姜沛沛. 结合景深估计的高斯衰减与自适应补偿去雾[J]. 光学精密工程, 2019,27(11):2439-2449.

Yan YANG, Guo-qiang ZHANG, Pei-pei JIANG. Gaussian decay and adaptive compensation dehazing algorithm combined with scene depth estimation[J]. Optics and precision engineering, 2019, 27(11): 2439-2449.
杨燕, 张国强, 姜沛沛. 结合景深估计的高斯衰减与自适应补偿去雾[J]. 光学精密工程, 2019,27(11):2439-2449. DOI： 10.3788/OPE.20192711.2439.

Yan YANG, Guo-qiang ZHANG, Pei-pei JIANG. Gaussian decay and adaptive compensation dehazing algorithm combined with scene depth estimation[J]. Optics and precision engineering, 2019, 27(11): 2439-2449. DOI： 10.3788/OPE.20192711.2439.

摘要

为了解决暗通道先验算法中对大片天空区域或大片白色区域失效的问题，提出一种结合景深估计的高斯衰减与自适应补偿去雾算法。首先通过RGB通道散射强度的算术平均与雾浓度近似正相关的关系估计场景深度；然后，结合景深的边缘信息，利用相邻像素之间的差异构造高斯滤波器对最小值通道进行滤波处理得到高斯暗通道；其次，利用高斯暗通道与其高斯函数之间的关系，通过调节因子与雾浓度呈负相关的关系，提出结合景深与高斯环绕函数卷积的策略获取自适应调节因子，从而对透射率进行自适应补偿估计；最后，结合大气散射模型复原无雾图像。实验结果表明，该算法在确保运行效率的基础上，可以准确的估计透射率，与经典算法相比较，在客观评价中可见边数平均提高了0.02，饱和像素点数平均下降了0.002。所提算法可以复原出自然清晰的无雾图像，尤其是在景深远处和天空区域取得了良好的效果。

Abstract

In order to solve the problem of large sky or white area failure in a dark channel prior algorithm

a Gaussian decay and adaptive compensation dehazing algorithm combined with scene depth estimation was proposed. Firstly

estimating the scene depth by an approximately positive correlation between the arithmetic mean of the RGB channel scattering intensities and the haze concentration. Then

combined with the edge information of the scene depth

a Gaussian filter is constructed using the difference between adjacent pixels to filter the minimum channel to obtain a Gaussian dark channel. Secondly

using the relationship between the Gaussian dark channel and its Gaussian function

through the relationship between the adjustment factor and the haze concentration

a strategy that combines convolution with scene depth and Gaussian surround function was proposed to obtain the adjustment factor; then

the transmission was adaptively compensated and estimated. Finally

the haze-free image was restored with the atmospheric scattering model. The experimental results show that the proposed algorithm can accurately estimate the transmission based on the operation efficiency. In the objective evaluation

the average number of edges increased by 0.02 while the number of saturated pixels decreased by 0.002. The proposed algorithm can also recover natural and clear haze-free images

especially in the scene depth and the sky area.

关键词

Keywords

references

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