Adaptive weighted multi-discriminator CycleGAN for underwater image enhancement

Jaihyun Park, David K. Han, Hanseok Ko

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


In this paper, we propose a novel underwater image enhancement method. Typical deep learning models for underwater image enhancement are trained by paired synthetic dataset. Therefore, these models are mostly effective for synthetic image enhancement but less so for real-world images. In contrast, cycle-consistent generative adversarial networks (CycleGAN) can be trained with unpaired dataset. However, performance of the CycleGAN is highly dependent upon the dataset, thus it may generate unrealistic images with less content information than original images. A novel solution we propose here is by starting with a CycleGAN, we add a pair of discriminators to preserve contents of input image while enhancing the image. As a part of the solution, we introduce an adaptive weighting method for limiting losses of the two types of discriminators to balance their influence and stabilize the training procedure. Extensive experiments demonstrate that the proposed method significantly outperforms the state-of-the-art methods on real-world underwater images.

Original languageEnglish
Article number200
JournalJournal of Marine Science and Engineering
Issue number7
Publication statusPublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 by the authors.


  • Generative adversarial networks
  • Image enhancement
  • Underwater

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Water Science and Technology
  • Ocean Engineering


Dive into the research topics of 'Adaptive weighted multi-discriminator CycleGAN for underwater image enhancement'. Together they form a unique fingerprint.

Cite this