Metalantis: A Comprehensive Underwater Image Enhancement Framework

• Published in: IEEE transactions on geoscience and remote sensing 2024-01, Vol.62, p.1-1
• Main Authors: Wang, Hao, Zhang, Weibo, Bai, Lu, Ren, Peng
• Format: Article
• Language: English
• Subjects: Adaptation models; Algorithms; Data models; depth map estimation; Estimation; Image acquisition; Image color analysis; Image enhancement; Phases; Reinforcement; Reinforcement learning; Submergence; Training; Underwater; underwater image enhancement; Virtual reality; Virtual underwater image synthesis
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3387722

Underwater images normally suffer from visual degradation issues such as color deviations, low contrasts, and blurred details. Recently, numerous underwater image enhancement algorithms have been proposed to address these issues. However, constrained by underwater conditions, acquiring non-underwater images and depth maps for underwater images is often challenging. This limitation significantly hampers the performance of data driven-based methods and physical model-based methods. Additionally, existing physical model-based methods typically require manual parameter settings, which tend to be bruteforce and insufficient to effectively address the diverse underwater scenes. To overcome these limitations, this paper presents a comprehensive underwater image enhancement framework comprising three phases: metamergence (i.e., meta submergence), metalief (i.e., meta relief), and metaebb (i.e., meta ebb). These phases are dedicated to virtual underwater image synthesis, underwater image depth map estimation, and the configuration of state-of-the-art physical models for underwater image enhancement by reinforcement learning, separately. While the three phases are trained separately, the former phase provides the necessary data for training the latter. We refer to the overall three phases as metalantis (i.e., meta Atlantis) because its training processes, involving variations from submergence via relief to ebb over indoor scenes, mimic the virtual variations of Atlantis. The metalantis framework empowers state-of-the-art physical models of underwater imaging through reinforcement learning with virtually generated data. The well-trained metalantis framework can take an underwater image as the sole input, process it into virtual representations, and finally enhance it. Comprehensive qualitative and quantitative empirical evaluations validate that our metalantis framework outperforms state-of-the-art underwater image enhancement methods. We release our code at https://gitee.com/wanghaoupc/Metalantis_UIE.