Phaseformer: Phase-Based Attention Mechanism for Underwater Image Restoration and Beyond

Raqib Khan, Anshul Negi, Ashutosh Kulkarni, Shruti S. Phutke, Santosh Kumar Vipparthi, Subrahmanyam Murala; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 9600-9611

Quality degradation is observed in underwater images due to the effects of light refraction and absorption by water leading to issues like color cast haziness and limited visibility. This degradation negatively affects the performance of autonomous underwater vehicles used in marine applications. To address these challenges we propose a lightweight phase-based transformer network with 1.77M parameters for underwater image restoration (UIR). Our approach focuses on effectively extracting non-contaminated features using a phase-based self-attention mechanism. We also introduce an optimized phase attention block to restore structural information by propagating prominent attentive features from the input. We evaluate our method on both synthetic (UIEB UFO-120) and real-world (UIEB U45 UCCS SQUID) underwater image datasets. Additionally we demonstrate its effectiveness for low-light image enhancement using the LOL dataset. Through extensive ablation studies and comparative analysis it is clear that the proposed approach outperforms existing state-of-the-art (SOTA) methods. The testing code is included in the supplementary material and will be publicly released upon acceptance.