Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)

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SSDC-GAN: Same Size Densely Connected GAN for Dehazing Network

Authors
Juan Wang1, 2, Chang Ding1, 2, Yonggang Ye1, 2, Minghu Wu1, 2, *, Zetao Zhang1, 2, Sheng Wang1, 2, Hao Yang1, 2, Ye Cao1, 2
1Hubei Energy Internet Engineering Technology Research Center, Hubei University of Technology, Wuhan, 430068, China
2Hubei Laboratory of Solar Energy Efficient Utilization and Energy Storage Operation Control, Hubei University of Technology, Wuhan, 430068, China
*Corresponding author. Email: wuxx1005@hbut.edu.cn
Corresponding Author
Minghu Wu
Available Online 28 August 2023.
DOI
10.2991/978-94-6463-222-4_35How to use a DOI?
Keywords
Image Dehazing; Densely Connected; Generative Adversarial Network (GAN)
Abstract

Haze weather negatively impacts the quality of external image collection and requires prompt resolution. However, most current deep learning image dehazing models struggle with restoring detail and color accuracy in real-world hazy images, hindering their practical application for high-quality image projects. To overcome this issue, we propose a novel connected mode (SSDC) for end-to-end dehazing that simplifies the problem to an image conversion task without relying on atmospheric scattering models or precise priors. The SSDC-GAN generator employs an encoder-decoder, same size densely connected architecture with residual blocks, and a depth discriminator to balance the relationship during training. Experimental results demonstrate that the proposed method performs favorably against state-of-the-art dehazing approaches on various benchmarks using real-world datasets O-HAZE and I-HAZE while preserving accurate contour and color information.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
28 August 2023
ISBN
10.2991/978-94-6463-222-4_35
ISSN
2589-4919
DOI
10.2991/978-94-6463-222-4_35How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Juan Wang
AU  - Chang Ding
AU  - Yonggang Ye
AU  - Minghu Wu
AU  - Zetao Zhang
AU  - Sheng Wang
AU  - Hao Yang
AU  - Ye Cao
PY  - 2023
DA  - 2023/08/28
TI  - SSDC-GAN: Same Size Densely Connected GAN for Dehazing Network
BT  - Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
PB  - Atlantis Press
SP  - 327
EP  - 337
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-222-4_35
DO  - 10.2991/978-94-6463-222-4_35
ID  - Wang2023
ER  -