Journal article
Adaptive Weighted Multi-Discriminator CycleGAN for Underwater Image Enhancement
Journal of marine science and engineering, v 7(7), p200
2019
Abstract
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.
Metrics
Details
- Title
- Adaptive Weighted Multi-Discriminator CycleGAN for Underwater Image Enhancement
- Creators
- Jaihyun Park - Korea UniversityDavid K. Han - Adelphi Laboratory CenterHanseok Ko - Korea University
- Publication Details
- Journal of marine science and engineering, v 7(7), p200
- Publisher
- Mdpi
- Number of pages
- 15
- Grant note
- FA2386-19-1-4001 / Air Force Office of Scientific Research; United States Department of Defense; Air Force Office of Scientific Research (AFOSR)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000478581900006
- Scopus ID
- 2-s2.0-85069177883
- Other Identifier
- 991021930832304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- International collaboration
- Web of Science research areas
- Engineering, Marine
- Engineering, Ocean
- Oceanography