Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs. In this paper, we propose window-based event denoising, which simultaneously deals with a stack of events while existing element-based denoising focuses on one event each time. Besides, we give the theoretical analysis based on probability distributions in both temporal and spatial domains to improve interpretability. In temporal domain, we use timestamp deviations between processing events and central event to judge the temporal correlation and filter out temporal-irrelevant events. In spatial domain, we choose maximum a posteriori (MAP) to discriminate real-world event and noise, and use the learned convolutional sparse coding to optimize the objective function. Based on the theoretical analysis, we build Temporal Window (TW) module and Soft Spatial Feature Embedding (SSFE) module to process temporal and spatial information separately, and construct a novel multi-scale window-based event denoising network, named MSDNet. The high denoising accuracy and fast running speed of our MSDNet enables us to achieve real-time denoising in complex scenes. Extensive experimental results verify the effectiveness and robustness of our MSDNet. Our algorithm can remove event noise effectively and efficiently and improve the performance of downstream tasks.

翻译：先前的基于深度学习的事件去噪方法大多因其复杂的架构设计而存在可解释性差及难以实时处理的问题。本文提出基于窗口的事件去噪方法，该方法可同时处理一组事件堆栈，而现有基于元素的方法每次仅处理一个事件。此外，我们从时间域和空间域的概率分布角度给出理论分析以提升可解释性。在时间域中，我们利用待处理事件与中心事件的时间戳偏差判断时间相关性，并滤除时间无关事件。在空间域中，我们选择最大后验概率(MAP)区分真实事件与噪声，并利用学习得到的卷积稀疏编码优化目标函数。基于理论分析，我们分别构建时间窗口(TW)模块和软空间特征嵌入(SSFE)模块处理时间和空间信息，并设计新型多尺度窗口事件去噪网络MSDNet。MSDNet的高去噪精度和快速运行速度使其能够在复杂场景中实现实时去噪。大量实验结果验证了MSDNet的有效性和鲁棒性。本算法能够高效去除事件噪声，并提升下游任务的性能。