A Semi-Supervised Domain-Adaptive Framework for Real-World Underwater Image Enhancement
Junjie Wen, Guidong Yang, Benyun Zhao, Dongyue Huang, Lei Lei, Bo Zhang, Zhi Yang Gao, Xi Chen, Ben M. Chen · IEEE Transactions on Geoscience and Remote Sensing · 2025
Underwater optical remote sensing is crucial for geoscience applications but often suffers from image degradation due to complex underwater environments. While learning-based methods have advanced underwater image enhancement (UIE), their efficacy in real-world UIE applications still faces challenges. This limitation arises from training predominantly on synthetic underwater images, resulting in a significantinter-domain gap when applied to real-world data. Additionally, diverse underwater conditions introduceintra-domain challenges, such as color casts and haze, further complicating the UIE process. To address these issues, we propose SSD-UIE, a semi-supervised domain-adaptive framework designed to mitigate bothinter- andintra-domain gaps. Our approach employs a systematic synthesis pipeline to reduce visualinter-domain discrepancies and introduces a Large Synthetic-Real Underwater Image Dataset (LSRUID) to facilitate the training of the framework. The Semantic-Blender is developed to handle semanticinter-domain differences, while the Intra-domain-aware Feature Extraction (IFE) branch and feature alignment strategy effectively addressintra-domain variability. Furthermore, the Dual-Trans Block is introduced to enhance the UIE performance while maintaining computational efficiency. Extensive experiments demonstrate that SSD-UIE outperforms state-of-the-art (SOTA) UIE methods in both qualitative and quantitative evaluations on real-world underwater images. Codes and dataset will be publicly available at https://github.com/RockWenJJ/SSD-UIE.git.