提出新型脉冲网络，提升水下图像增强的全局感知与细节保留能力
UIESNN: A Scale-Aware Spiking Network for Underwater Image Enhancement
- 设计多尺度池化脉冲块，增强感受野并保持细节
- 在EUVP和LSUI数据集上达到SNN方法最优效果
- 适合对能效敏感的水下视觉系统研究者
水下图像增强（UIE）是脉冲神经网络（SNN）中一个实用但研究不足的应用，主要退化特征为大尺度、低频问题，如波长依赖的色偏和散射引起的遮蔽效应。现有SNN复原方法依赖局部脉冲感知，限制了全局修正能力，导致表现饱和或不一致。为此，我们提出针对UIE的尺度感知脉冲网络框架UIESNN。其核心为多尺度池化LIF模块（MPLB），将分层多尺度池化响应注入膜电位动态，扩大有效感受野的同时保留细粒度细节，并诱导异构的尺度依赖激活。在此基础上，构建全脉冲驱动的残差架构，融合频率分解与注意力精修。在EUVP和LSUI基准上的大量实验表明，UIESNN在SNN方法中达到最先进性能，显著提升色彩保真度与空间一致性，同时具备有竞争力的能耗表现。
原文摘要 · Abstract (English)
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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