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

TL;DR

Phaseformer introduces a lightweight, phase-based transformer network that significantly improves underwater image restoration and low-light enhancement by effectively extracting features and restoring structural details, outperforming existing methods.

Contribution

The paper presents a novel phase-based self-attention mechanism within a lightweight transformer for underwater image restoration and low-light enhancement, with extensive validation on multiple datasets.

Findings

Outperforms state-of-the-art methods in underwater image restoration

Effective in low-light image enhancement tasks

Uses only 1.77M parameters, demonstrating efficiency

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.