Phaseformer: Phase-based Attention Mechanism for Underwater Image Restoration and Beyond
This addresses image quality problems for autonomous underwater vehicles in marine applications, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles underwater image degradation issues like color cast and haziness by proposing a lightweight phase-based transformer network with 1.77M parameters, which outperforms state-of-the-art methods on synthetic and real-world datasets and also shows effectiveness in low-light image enhancement.
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.