Image dehazing method based on adaptive bi-channel priors

Yutong JIANG; Zhonglin YANG; Mengqi ZHU; Yi ZHANG; Lixia GUO

doi:10.37188/OPE.20223010.1246

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Image dehazing method based on adaptive bi-channel priors

Information Sciences | 更新时间：2022-06-21

- Image dehazing method based on adaptive bi-channel priors
- Optics and Precision Engineering Vol. 30, Issue 10, Pages: 1246-1262(2022)
- 作者机构：
  
  中国北方车辆研究所，北京 100072
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20223010.1246
  CLC： TP391.4
- Received：08 July 2021，
  
  Revised：11 August 2021，
  
  Published：25 May 2022
- 稿件说明：
移动端阅览
姜雨彤,杨忠琳,朱梦琪等.适应性双通道先验的图像去雾方法[J].光学精密工程,2022,30(10):1246-1262.

JIANG Yutong,YANG Zhonglin,ZHU Mengqi,et al.Image dehazing method based on adaptive bi-channel priors[J].Optics and Precision Engineering,2022,30(10):1246-1262.
姜雨彤,杨忠琳,朱梦琪等.适应性双通道先验的图像去雾方法[J].光学精密工程,2022,30(10):1246-1262. DOI： 10.37188/OPE.20223010.1246.

JIANG Yutong,YANG Zhonglin,ZHU Mengqi,et al.Image dehazing method based on adaptive bi-channel priors[J].Optics and Precision Engineering,2022,30(10):1246-1262. DOI： 10.37188/OPE.20223010.1246.

摘要

图像是现代化战争的重要信息来源，雾天环境下图像质量下降，严重妨碍光电侦察识别能力。为提高雾气环境下图像有效利用性，开展了适应性双通道先验的图像去雾方法研究。首先，以暗通道先验理论与亮通道先验理论为基础，将有雾图像从 RGB 空间转换到 HSV 颜色空间，使用饱和度和亮度分量的阈值来检测有雾图像中分别不满足暗通道先验和亮通道先验的白色或亮色像素点和黑色或暗色像素点；然后，选用超像素作为暗通道和亮通道计算的局部区域，估计局部透射率和大气光值；最后，由于亮暗双通道方法对白色和黑色像素点的透射率和大气光值进行错误估计，采用本文提出的适应性双通道先验方法进行矫正，通过导引滤波器对透射率图和大气光图进行滤波，代入到大气散射模型中，求得清晰的去雾图像。实验结果表明，去雾后的图像恢复了真实颜色、视觉效果自然、清晰，准确高效地实现图像的去雾处理；在FRIDA数据集上进行去雾处理，采用本文方法的去雾图像与真值的均方误差优于现有方法，相较于双通道先验去雾方法的均方差值降低了15%。

Abstract

Image is an important source of information for modern warfare

and the quality of image decreases in foggy environment

which seriously hinders the ability of photoelectric reconnaissance and identification. In order to improve the effective utilization of images in foggy environment

an adaptive bi-channel prior image dehazing method was developed. First

based on the dark channel prior and the bright channel prior theories

the hazy images are converted from RGB to HSV color space

and the thresholds of saturation and luminance components are used to detect white or light pixels and black or dark pixels in hazy images that do not satisfy the dark and light channel priors

respectively. Then

superpixels are selected as the local area for the calculation of the dark and bright channels

and the local transmittance and atmospheric light values are estimated. Finally

adaptive bi-channel priors are developed to rectify any incorrect estimation of transmission and atmospheric light values for both white and black pixels. The transmittance map and atmospheric light map are filtered by the guided filter

and then substituted into the atmospheric scattering model to obtain a clear dehaze image. Experimental results show that the dehazed image restores the true color

the visual effect is natural and clear

and the dehazing process of the image is accurately and efficiently achieved. The dehazing process is performed on the FRIDA database

the mean square error between the dehazed image and the ground truth using the method in this paper is better than that of the existing method

which are 15% lower than that yielded by the BiCP method.

关键词

Keywords

references

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