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Single image dehazing algorithm based on generative adversarial network

Published: 20 August 2022 Publication History

Abstract

This paper proposes a kind of generative adversarial network which is used to remove the haze for single image. In this paper, the generator uses U-Net as the backbone, and in order to effectively fuse the feature of different scales between the non-adjacent layers of the generator, a dense linking module which based on back-projection is used in the generator. In this paper, a kind of enhancement strategy which based on boosting strategy is used to improve the effectiveness of skip connection between the encoder and the decoder in the generator model. In order to evaluate the effect of haze removing, the proposed model is trained on the RESIDE and evaluated on the SOTS. The experiment proves that our method has advantages in both qualitative comparison and quantitative assessment.

References

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Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. Benchmarking single- image dehazing and beyond. TIP, 28(1):492–505, 2019. 5, 6, 7
[2]
Kaiming He, Jian Sun, and Xiaoou Tang. Single image haze removal using dark channel prior. TPAMI, 33(12):2341– 2353, 2010. 2, 3, 6, 7
[3]
Tan, R. T. "Visibility in bad weather from a single image." 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 24-26 June 2008, Anchorage, Alaska, USA IEEE, 2008
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Li, B., "AOD-Net: All-in-One Dehazing Network." 2017 IEEE International Conference on Computer Vision (ICCV) IEEE, 2017.
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Radford, A., L. Metz, and S. Chintala . "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks." Computer ence (2015)
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Isola, P., "Image-to-Image Translation with Conditional Adversarial Networks." IEEE (2016)
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Hang Dong, Jinshan Pan, Lei Xiang, Zhe Hu, Xinyi Zhang, Fei Wang, and Ming-Hsuan Yang. Multi-scale boosted dehazing network with dense feature fusion. In CVPR, 2020. 1, 2, 3, 4, 5, 6, 7, 8
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Zhu, Q., Mai, J. and Shao, L. J. I. t. o. i. p. A fast single image haze removal algorithm using color attenuation prior, 24, 11 (2015), 3522-3533
[9]
Kangfu Mei, Aiwen Jiang, Juncheng Li, and Mingwen Wang. Progressive feature fusion network for realistic image dehazing. In Asian Conference on Computer Vision, pages 203–215,2018
[10]
Xiaohong Liu, Yongrui Ma, Zhihao Shi, and Jun Chen. Griddehazenet: Attention-based multi-scale network for image dehazing. In ICCV, 2019. 1, 2, 3, 4, 5, 6, 7, 8

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  • (2023)Estimation of Air Light With Deep Learning for a Near Real-Time Image Dehazing SystemEstimation of Air Light With Deep Learning for a Near Real-Time Image Dehazing SystemBlack Sea Journal of Engineering and Science10.34248/bsengineering.13496436:4(604-612)Online publication date: 15-Oct-2023

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PRIS '22: Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems
July 2022
102 pages
ISBN:9781450396080
DOI:10.1145/3549179
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2022

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Author Tags

  1. Dehazing
  2. Generative adversarial network
  3. U-Net

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  • (2023)Estimation of Air Light With Deep Learning for a Near Real-Time Image Dehazing SystemEstimation of Air Light With Deep Learning for a Near Real-Time Image Dehazing SystemBlack Sea Journal of Engineering and Science10.34248/bsengineering.13496436:4(604-612)Online publication date: 15-Oct-2023

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