用轻量脉冲神经网络提升事件相机的分辨率，适合边缘设备实时运行。
Ultralight Polarity-Split Neuromorphic SNN for Event-Stream Super-Resolution
- 通过极性拆分编码将正负事件分流处理，降低模型体积。
- 在多个数据集上实现媲美主流方法的超分辨率效果，推理更快。
- 适合部署在资源受限的事件相机或作为视觉任务前端预处理。
事件相机具备高时间分辨率、低延迟和高动态范围等优势，但其有限的空间分辨率制约了细粒度感知任务。本文提出一种基于脉冲神经网络（SNN）的超轻量级流式事件到事件超分辨率方法，专为资源受限设备的实时部署设计。为减少模型规模，提出新型双前向极性拆分事件编码策略，将正负事件通过共享的SNN分别沿独立路径处理。同时引入可学习时空极性感知损失（LearnSTPLoss），利用可学习的不确定性权重自适应平衡时间、空间与极性一致性。实验表明，该方法在多个数据集上达到竞争力的超分辨率性能，显著降低模型大小与推理时间。轻量化设计使其可嵌入事件相机或作为下游视觉任务的高效前端预处理模块。
原文摘要 · Abstract (English)
Event cameras offer unparalleled advantages such as high temporal resolution, low latency, and high dynamic range. However, their limited spatial resolution poses challenges for fine-grained perception tasks. In this work, we propose an ultra-lightweight, stream-based event-to-event super-resolution method based on Spiking Neural Networks (SNNs), designed for real-time deployment on resource-constrained devices. To further reduce model size, we introduce a novel Dual-Forward Polarity-Split Event Encoding strategy that decouples positive and negative events into separate forward paths through a shared SNN. Furthermore, we propose a Learnable Spatio-temporal Polarity-aware Loss (LearnSTPLoss) that adaptively balances temporal, spatial, and polarity consistency using learnable uncertainty-based weights. Experimental results demonstrate that our method achieves competitive super-resolution performance on multiple datasets while significantly reducing model size and inference time. The lightweight design enables embedding the module into event cameras or using it as an efficient front-end preprocessing for downstream vision tasks.
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