Single Underwater Image Restoration via Unsupervised Generative Adversarial Network and Contrastive Learning

Yi Hung Sung*, Li Wei Kang*, Chia Hung Yeh

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

摘要

Underwater image restoration has gained more and more attention recently due to its several applications in marine environmental surveillance-related tasks. In this paper, a novel unsupervised GAN (generative adversarial network)-based deep learning framework for single underwater image restoration is proposed. Without needing paired training images, we introduce contrastive learning with feature and style reconstruction loss functions in our unsupervised GAN-based structure to learn an image generator for translating underwater images to the corresponding in-air images. Extensive experiments have shown that the proposed method outperforms (or is comparable with) the state-of-the-art deep learning-based methods relying on paired/unpaired training data quantitatively and qualitatively.

原文英語
主出版物標題2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面853-854
頁數2
ISBN(電子)9798350324174
DOIs
出版狀態已發佈 - 2023
事件2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Pingtung, 臺灣
持續時間: 2023 7月 172023 7月 19

出版系列

名字2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

會議

會議2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
國家/地區臺灣
城市Pingtung
期間2023/07/172023/07/19

ASJC Scopus subject areas

  • 人工智慧
  • 人機介面
  • 資訊系統
  • 資訊系統與管理
  • 電氣與電子工程
  • 媒體技術
  • 儀器

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