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

• URL: http://arxiv.org/abs/2605.08376v1
• Date: Fri, 08 May 2026 18:34:14 GMT
• Title: UIESNN: A Scale-Aware Spiking Network for Underwater Image Enhancement
• Abstract summary: We propose a scale-aware SNN framework for underwater image enhancement named UIESNN.<n>At its core is a Multi-scale Pooling LIF Block (MPLB) that injects hierarchical multi-scale pooling responses into membrane dynamics.<n>Building on MPLB, we design a spiking residual architecture that integrates frequency decomposition and attention-based refinement.
• Score: 11.773752327675483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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