Aerial image dehazing using improved dark channel prior

Hao-nan HAN; Feng QIAN; Jian-wei LÜ; Bao ZHANG

doi:10.3788/OPE.20202806.1387

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Aerial image dehazing using improved dark channel prior

更新时间：2020-07-13

- Aerial image dehazing using improved dark channel prior
- Optics and Precision Engineering Vol. 28, Issue 6, Pages: 1387-1394(2020)
- 作者机构：
  
  1.中国科学院大学，北京 100049
  2.中国科学院长春光学精密机械与物理研究所，吉林长春 130033
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20202806.1387
  CLC： TP394.1; TH691.9
- Received：26 November 2019，
  
  Accepted：05 February 2020，
  
  Published：15 June 2020
- 稿件说明：
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Hao-nan HAN, Feng QIAN, Jian-wei LÜ, et al. Aerial image dehazing using improved dark channel prior[J]. Optics and precision engineering, 2020, 28(6): 1387-1394.
DOI：

Hao-nan HAN, Feng QIAN, Jian-wei LÜ, et al. Aerial image dehazing using improved dark channel prior[J]. Optics and precision engineering, 2020, 28(6): 1387-1394. DOI： 10.3788/OPE.20202806.1387.

摘要

目前较为流行的去雾算法都存在着过度增强以及增强不足，容易造成光晕效应以及色彩严重失真。提出一种基于四叉树细分的改进大气光估计方法以及一种改进的引导滤波用来解决这些问题。首先，对非重叠暗通道使用四叉树细分方法估计更加可靠的大气光值。然后，分析引导滤波在边缘区域的光晕效应产生的原因，对其加入自适应权重因子，用改进后的引导滤波对初始传输图进行优化。最后，用估计的大气光值和优化后的传输图根据大气散射模型得到去雾图像。实验结果表明：去雾后的图像颜色较为可靠，边缘区域光晕效应减弱。从颜色可靠性和细节增强度来说，提出的算法比现阶段的去雾算法有较为出众的表现。

Abstract

Most existing dehazing algorithms suffer from under-or over-enhancement

color distortion

and halo artifacts. An improved method of atmospheric light estimation using quad-tree subdivision and an improved guided filter were proposed to solve these problems. First

a more faithful estimate of global atmospheric light was produced by quad-tree subdivision using a non-overlapped dark channel. Then

the reasons for the existence of halo artifacts in edge regions were discussed and an adaptive weight was added to the guided image filter. The improved guided image filter was used to refine the raw transmission map. Finally

based on the atmospheric scattering model

a dehazed image was obtained using the estimated atmospheric light value and refined transmission map. Experimental results indicate that the color of the dehazed image is more reliable and halo artifacts in edge regions are reduced. The proposed algorithm performs better than state-of-the-art haze removal algorithms in terms of color fidelity and detail enhancement.

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references

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