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arXiv · 2605.08376

UIESNN: A Scale-Aware Spiking Network for Underwater Image Enhancement

Shuang Chen·Ruochen Li·Zihan Zhu·Ronald Thenius·Farshad Arvin·Amir Atapour-Abarghouei
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Abstract

Underwater image enhancement (UIE) is a practically important yet underexplored application of spiking neural networks (SNNs), where the dominant degradations are large-scale and low-frequency, such as wavelength-dependent colour casts and scattering-induced veiling. Existing SNN restoration designs rely on locally bounded spiking perception, which can limit global correction and lead to saturated or inconsistent representations. To address these challenges, we propose a scale-aware SNN framework for UIE named UIESNN. At its core is a Multi-scale Pooling LIF Block (MPLB) that injects hierarchical multi-scale pooling responses into membrane dynamics, thereby enlarging the effective receptive field while preserving fine-grained details and inducing heterogeneous scale-dependent activations. Building on MPLB, we design a spiking residual architecture that integrates frequency decomposition and attention-based refinement in a fully spike-driven pipeline. Extensive experiments on the EUVP and LSUI benchmarks demonstrate that UIESNN achieves state-of-the-art performance among SNN-based methods, delivering improved colour fidelity and spatial coherence with competitive energy cost.

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Shuang Chen, Ruochen Li, Zihan Zhu, Ronald Thenius, Farshad Arvin, Amir Atapour-Abarghouei. 2026-05-08. UIESNN: A Scale-Aware Spiking Network for Underwater Image Enhancement. https://arxiv.org/abs/2605.08376

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