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.

提出了一种基于窗口的事件去噪方法，其中通过使用时间和空间域的概率分布进行理论分析来提高解释性，建立了时间窗口（TW）模块和软空间特征嵌入（SSFE）模块来分别处理时间和空间信息，构建了一种名为MSDNet的新型多尺度基于窗口的事件去噪网络，具有高去噪精度和快速运行速度，在复杂场景中实现了实时去噪，实验证明了算法的有效性和鲁棒性，能够有效高效去除事件噪声并提高下游任务的性能。

基于时空相关增强的快速窗口事件去噪