TY - GEN
T1 - Single Underwater Image Restoration via Unsupervised Generative Adversarial Network and Contrastive Learning
AU - Sung, Yi Hung
AU - Kang, Li Wei
AU - Yeh, Chia Hung
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - contrastive learning
KW - deep learning
KW - generative adversarial networks
KW - single underwater image restoration
KW - unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85174975994&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174975994&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Taiwan58799.2023.10226658
DO - 10.1109/ICCE-Taiwan58799.2023.10226658
M3 - Conference contribution
AN - SCOPUS:85174975994
T3 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
SP - 853
EP - 854
BT - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
Y2 - 17 July 2023 through 19 July 2023
ER -
