Phaseformer: Phase-based Attention Mechanism for Underwater Image Restoration and Beyond
Journal:
arXiv
Published Date:
Dec 2, 2024
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.
