UIESNN: A Scale-Aware Spiking Network for Underwater Image Enhancement
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866909028325523456 |
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| author | Chen, Shuang Li, Ruochen Zhu, Zihan Thenius, Ronald Arvin, Farshad Atapour-Abarghouei, Amir |
| author_facet | Chen, Shuang Li, Ruochen Zhu, Zihan Thenius, Ronald Arvin, Farshad Atapour-Abarghouei, Amir |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_08376 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | UIESNN: A Scale-Aware Spiking Network for Underwater Image Enhancement Chen, Shuang Li, Ruochen Zhu, Zihan Thenius, Ronald Arvin, Farshad Atapour-Abarghouei, Amir Computer Vision and Pattern Recognition 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. |
| title | UIESNN: A Scale-Aware Spiking Network for Underwater Image Enhancement |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.08376 |