PUGAN : Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators

Runmin CONG, Wenyu YANG, Wei ZHANG*, Chongyi LI*, Chun-Le GUO, Qingming HUANG, Sam KWONG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

14 Citations (Scopus)


Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement sub-network (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://rmcong.github.io/proj_PUGAN.html.

Original languageEnglish
Pages (from-to)4472-4485
Number of pages14
JournalIEEE Transactions on Image Processing
Early online date19 Jun 2023
Publication statusPublished - 2023
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by the National Key Research and Development Program of China under Grant 2021ZD0112100; in part by the National Natural Science Foundation of China under Grant 62002014.

Publisher Copyright:
© 1992-2012 IEEE.


  • degradation quantization
  • generative adversarial network
  • physical model
  • Underwater image enhancement


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