Phaseformer: Phase-based attention mechanism for underwater image restoration and beyond

MD Raqib Khan, Anshul Negi, Ashutosh Kulkarni, Shruti Shantiling Phutke, Santosh Kumar Vipparthi, Subrahmanyam Murala

Abstract:

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. Code is available at https://github.com/Mdraqibkhan/Phaseformer.
Keywords: Phase-attention, Multi-head Attention, Underwater Image Restoration