  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.
