Multi-Prior Fusion Transfer Plugin for Adapting In-Air Models to Underwater Image Enhancement and Detection
Jingchun Zhou, Dehuan Zhang, Zongxin He, Qilin Gai, Qiuping Jiang
Abstract
Underwater data is inherently scarce and exhibits complex distributions, making it challenging to train high-performance models from scratch. In contrast, in-air models are structurally mature, resource-rich, and offer strong potential for transfer. However, significant discrepancies in visual characteristics and feature distributions between underwater and in-air environments often lead to severe performance degradation when applying in-air models directly. To address this issue, we propose IA2U, a lightweight plugin designed for efficient underwater adaptation without modifying the original model architecture. IA2U can be flexibly integrated into arbitrary in-air networks, offering high generalizability and low deployment costs. Specifically, IA2U incorporates three types of prior knowledge-water type, degradation pattern, and sample semantics-which are embedded into intermediate layers through feature injection and channel-wise modulation to guide the network's response to underwater-specific features. Furthermore, a multi-scale feature alignment module is introduced to dynamically balance information across different resolution paths, enhancing consistency and contextual representation. Extensive experiments demonstrate that IA2U significantly improves both image enhancement and object detection performance. Specifically, on the UIEB dataset, IA2U boosts Shallow-UWNet by 5.2 dB in PSNR and reduces LPIPS by 52%; on the RUOD dataset, it increases AP by 1.8% when applied to the PAA detector. IA2U provides an effective and scalable solution for building robust underwater perception systems with minimal adaptation costs. Our code is available at https://github.com/zhoujingchun03/IA2U.