Dehazing using a decomposition-composition and recurrent refinement network based on the physical imaging model

Yan-ru FENG; Yi-bin WANG

doi:10.37188/OPE.20212911.2692

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Dehazing using a decomposition-composition and recurrent refinement network based on the physical imaging model

Information Sciences | 更新时间：2022-01-25

- Dehazing using a decomposition-composition and recurrent refinement network based on the physical imaging model
- Optics and Precision Engineering Vol. 29, Issue 11, Pages: 2692-2702(2021)
- 作者机构：
  
  1.防灾科技学院信息工程学院，河北三河 065201
  2.四川师范大学工学院，四川成都 610068
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20212911.2692
  CLC： TP391.41
- Received：21 May 2021，
  
  Revised：28 June 2021，
  
  Published：15 November 2021
- 稿件说明：
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冯燕茹,王一斌.物理成像模型的分解合成循环细化去雾网络[J].光学精密工程,2021,29(11):2692-2702.

FENG Yan-ru,WANG Yi-bin.Dehazing using a decomposition-composition and recurrent refinement network based on the physical imaging model[J].Optics and Precision Engineering,2021,29(11):2692-2702.
冯燕茹,王一斌.物理成像模型的分解合成循环细化去雾网络[J].光学精密工程,2021,29(11):2692-2702. DOI： 10.37188/OPE.20212911.2692.

FENG Yan-ru,WANG Yi-bin.Dehazing using a decomposition-composition and recurrent refinement network based on the physical imaging model[J].Optics and Precision Engineering,2021,29(11):2692-2702. DOI： 10.37188/OPE.20212911.2692.

摘要

为了充分挖掘雾天成像时的先验信息和物理参数间的约束关系，提高去雾算法的精度，本文提出了嵌入物理成像模型的分解合成循环细化网络以实现图像去雾。不同于已有的去雾算法，它包含透射率估计分支和清晰图像估计分支，且两分支均使用嵌入循环单元的多尺度金字塔编码解码网络框架来实现，具有能加强循环间信息交流、充分利用多尺度上下文特征的优点。考虑到透射率与场景深度和雾气浓度有关，可将透射率视为雾浓度先验，引导清晰图像估计分支循环细化去雾结果；而清晰图像中包含场景的深度信息，可将其视为深度先验，引导透射率估计分支预测及循环细化透射率。每次循环时，两分支估计的透射率和清晰图像进一步合成雾图，循环作为网络的输入，以确保透射率和清晰图像的估计结果满足物理成像模型的约束。实验结果表明算法在合成雾图及真实图像上均能取得较好的去雾效果，在视觉评价和客观评价方面均优于现有的去雾算法，单张雾图的处理时间仅为0.037 s，能有效用于图像去雾的工程实践中。

Abstract

To explore the dehazing priors and constraints among the physical parameters during imaging under haze conditions and improve dehazing accuracy， we propose a decomposition–composition and recurrent refinement network based on the physical imaging model for image dehazing. Unlike existing dehazing methods， it contains a transmission prediction branch and a clear image prediction branch. Both branches are built based on the multi-scale pyramid encoder–decoder network with a recurrent unit that can utilize multiscale contextual features and has more complete information exchange. Considering the transmission map is related to the scene depth and haze concentration， the transmission map can be regarded as a haze concentration prior and guide the clear image prediction branch to estimate and refine the dehazing result recurrently. Similarly， the clear image that contains the scene depth information is regarded as a depth prior and guides the transmission map prediction branch to predict and refine the transmission map. Then， the predicted transmission map and clear image are further synthesized as the haze image that serves as the input of the network in each recurrent step， enabling the predicted transmission map and clear image to meet the constraints of the physical imaging model. The experimental results demonstrate that our method not only achieves a good dehazing effect on both synthetic and real images， but also outperforms existing methods in terms of quality and quantity. The average processing time for a single hazy image is 0.037 s， indicating that it has potential application value in the engineering practice of image dehazing.

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references

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