An Improved Dark Channel Prior Dehazing Algorithm Based on Superpixel Image Segmentation

JIN Tian-hu; TAO Yan-yun; LI Zuo-yong

doi:10.12263/DZXB.20211566

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An Improved Dark Channel Prior Dehazing Algorithm Based on Superpixel Image Segmentation

PAPERS | 更新时间：2025-07-02

- An Improved Dark Channel Prior Dehazing Algorithm Based on Superpixel Image Segmentation
- ACTA ELECTRONICA SINICA Vol. 51, Issue 1, Pages: 146-159(2023)
- 作者机构：
  
  1.苏州大学轨道交通学院，江苏苏州 215137
  2.福建省信息处理与智能控制重点实验室（闽江学院），福建闽江 350108
  3.南京大学计算机软件新技术国家重点实验室，江苏南京 215311
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20211566
  CLC： TP391;
- Received：22 November 2021，
  
  Revised：2022-02-20，
  
  Published：25 January 2023
- 稿件说明：
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金天虎,陶砚蕴,李佐勇.基于超像素图像分割的暗通道先验去雾改进算法[J].电子学报,2023,51(01):146-159.

JIN Tian-hu,TAO Yan-yun,LI Zuo-yong.An Improved Dark Channel Prior Dehazing Algorithm Based on Superpixel Image Segmentation[J].ACTA ELECTRONICA SINICA,2023,51(01):146-159.
金天虎,陶砚蕴,李佐勇.基于超像素图像分割的暗通道先验去雾改进算法[J].电子学报,2023,51(01):146-159. DOI： 10.12263/DZXB.20211566.

JIN Tian-hu,TAO Yan-yun,LI Zuo-yong.An Improved Dark Channel Prior Dehazing Algorithm Based on Superpixel Image Segmentation[J].ACTA ELECTRONICA SINICA,2023,51(01):146-159. DOI： 10.12263/DZXB.20211566.

摘要

本文针对暗通道先验去雾算法在天空等明亮区域存在明显的噪声放大和色彩失真的问题，从大气散射模型和暗通道先验理论出发修正大气光值和非暗通道区域透射率，提出了基于超像素图像分割的暗通道先验去雾改进算法.本文算法基于大气散射模型和暗通道先验理论建立雾天成像模型；通过超像素阈值分割算法将图像分为暗通道区域和非暗通道区域，暗通道区域（Dark Channel Region，DCR）即图像中符合暗通道先验理论的部分，非暗通道区域（Non-Dark Channel Region，NDCR）即图像中不符合暗通道先验理论的部分；再分别通过非暗通道区域和暗通道区域的超像素，估计全局大气光值，修正非暗通道区域透射率；最终根据大气散射模型恢复无雾图像.本文算法提高了全局大气光值的准确性，有效抑制了天空等非暗通道区域的失真，复原图像更加清晰自然，增强了视觉效果.主观和客观评价的实验表明，本文算法能够取得比传统算法更优的去雾效果.

Abstract

In view of the obvious noise amplification and color distortion of dark channel prior defogging algorithm in bright areas such as sky

this paper proposes an improved dark channel prior defogging algorithm based on superpixel image segmentation. Based on the atmospheric scattering model and dark channel prior theory

the atmospheric light value and the transmittance of non-dark channel region are modified. Firstly

a foggy imaging model is established based on atmospheric scattering model and dark channel prior theory. Then

the image is divided into dark channel region and non-dark channel region through the super pixel threshold segmentation algorithm. Dark channel region (DCR) is the part of the image conforming to the dark channel prior theory. Non-dark channel region (NDCR) is the part of the image that does not conform to the dark channel prior theory. Then the global atmospheric light value is estimated by the superpixels of the non-dark channel region and the dark channel region

respectively

and then the transmittance of the non-dark channel region is corrected. Finally

the fog-free image is restored according to the atmospheric scattering model. The algorithm in this paper improves the accuracy of global atmospheric light values

and effectively inhibits the distortion of sky and other non-dark channel regions

making the restored image clearer and more natural

and also enhances the visual effect. Subjective and objective evaluation experiments show that the proposed algorithm can achieve better defogging effect than the traditional algorithm.

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references

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