Accelerated haze removal for a single image by dark channel prior

Bo-xuan YUE; Kang-ling LIU; Zi-yang WANG; Jun LIANG

doi:10.1631/FITEE.1700148

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Accelerated haze removal for a single image by dark channel prior

Regular Papers | Updated：2022-05-19

- Accelerated haze removal for a single image by dark channel prior
- 基于暗通道先验的单幅图像快速去雾算法
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 8, Pages: 1109-1118(2019)
- Affiliations：
  
  State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  [ "Bo-xuan YUE, E-mail: ybx90@outlook.com" ]
  Jun LIANG, E-mail:jliang@iipc.zju.edu.cn
- Funds：
- DOI：10.1631/FITEE.1700148
  CLC： TP751
- Received：27 February 2017，
  
  Revised：2019-;8-15，
  
  Published：2019-08
- Accepted：
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Bo-xuan YUE, Kang-ling LIU, Zi-yang WANG, et al. Accelerated haze removal for a single image by dark channel prior[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1109-1118.
DOI：

Bo-xuan YUE, Kang-ling LIU, Zi-yang WANG, et al. Accelerated haze removal for a single image by dark channel prior[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1109-1118. DOI： 10.1631/FITEE.1700148.

摘要

在雾气中，可见光的散射降低了图像可见度。目前，去雾仍是图像处理应用的一个挑战。为实现去雾，提出一种基于单个像素的去雾加速算法。不同于基于区块的方法，所提方法分别估计每个区域的变换矩阵和大气光参数，其中加速的关键在于所有参数能在一次遍历中获得。然后，对传输映射进行双边过滤，恢复像素之间的关系。通过线性模糊模型恢复后，对恢复的图像进行调整，以提高对比度、光照强度和饱和度，尤其是补偿由相应波长引起的不同通道的光强误差。实验结果表明，该方法在处理速率方面优于已有的最先进去雾算法。与其他去雾方法比较和引入定量准则（峰信噪比、可检测边际速率、信息熵差）验证该方法有效。

Abstract

Haze scatters light transmitted in the air and reduces the visibility of images. Dealing with haze is still a challenge for image processing applications nowadays. For the purpose of haze removal

we propose an accelerated dehazing method based on single pixels. Unlike other methods based on regions

our method estimates the transmission map and atmospheric light for each pixel independently

so that all parameters can be evaluated in one traverse

which is a key to acceleration. Then

the transmission map is bilaterally filtered to restore the relationship between pixels. After restoration via the linear hazy model

the restored images are tuned to improve the contrast

value

and saturation

in particular to offset the intensity errors in different channels caused by the corresponding wavelengths. The experimental results demonstrate that the proposed dehazing method outperforms the state-of-the-art dehazing methods in terms of processing speed. Comparisons with other dehazing methods and quantitative criteria (peak signal-to-noise ratio

detectable marginal rate

and information entropy difference) are introduced to verify its performance.

关键词

Keywords

references

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