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

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arxiv 2412.01456 v1 pith:OVQAOQNB submitted 2024-12-02 cs.CV eess.IV

MD Raqib Khan , Anshul Negi , Ashutosh Kulkarni , Shruti S. Phutke , Santosh Kumar Vipparthi , Subrahmanyam Murala This is my paper · ORCID
classification cs.CVeess.IV
keywords underwaterimagephase-basedapproachattentiondegradationfeaturesmechanism
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Single-Step Latent Diffusion for Underwater Image Restoration

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SLURPP combines pretrained latent diffusion priors with a physics-based scene-medium decomposition to restore underwater images in one inference step, beating prior diffusion methods in speed and quality.

  2. DEEP-SEA: Deep-Learning Enhancement for Environmental Perception in Submerged Aquatics

    cs.CV 2025-08 unverdicted novelty 4.0 of 10

    DEEP-SEA, a dual-frequency self-attention network, reports state-of-the-art underwater image restoration on the EUVP and LSUI benchmarks.

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