A Lightweight Polarization-Guided Plug-In for Underwater Image Enhancement (TCSVT 2025)
Abstract
Underwater images play a vital role in marine exploration, but are often severely degraded due to complex imaging conditions, including color distortion, haze effects, and nonuniform illumination. Existing deep learning-based enhancement methods predominantly rely on conventional RGB sensors, which struggle to distinguish between scattered and reflected light, thereby limiting enhancement performance. Polarization imaging, with its capability to capture directional light information, offers promising potential for underwater image enhancement. In this paper, we propose a lightweight yet effective polarization feature extractor that captures global spatial cues from polarization images. Additionally, we design a polarization-guided feature integration module that adaptively enhances the representational capacity of RGB features. Notably, the proposed module is plug-in and can be seamlessly integrated into existing RGB-based enhancement networks. Extensive experiments across multiple datasets demonstrate that incorporating polarization information significantly improves enhancement performance, highlighting its effectiveness as a valuable cue for underwater image enhancement. The code and pretrained models are at https://github.com/jgy0/UPGD
