用相位注意力机制提升水下图像清晰度，轻量高效且泛化性强。
Phaseformer: Phase-based Attention Mechanism for Underwater Image Restoration and Beyond
- 基于相位的自注意力机制，精准提取未受污染特征。
- 仅177万参数，实测在多个水下数据集上超越现有最佳方法。
- 不仅适用于水下图像，还能有效增强低光图像，适合海洋智能系统使用。
水下图像因光线折射与水体吸收导致色彩偏差、朦胧感和可见度下降，严重影响自主水下航行器在海洋应用中的性能。为此，我们提出一种参数量仅为1.77M的轻量级相位注意力变换器网络，用于水下图像恢复（UIR）。该方法通过相位基自注意力机制有效提取未受污染特征，并引入优化的相位注意力模块，以传播显著注意力特征来恢复结构信息。我们在合成数据集（UIEB、UFO-120）和真实世界数据集（UIEB、U45、UCCS、SQUID）上进行评估，同时在低光照图像增强数据集（LOL）上验证其泛化能力。大量消融实验与对比分析表明，所提方法显著优于现有SOTA方法。
原文摘要 · Abstract (English)
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.
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