Event Stream Super-Resolution (ESR) aims to address the challenge of insufficient spatial resolution in event streams, which holds great significance for the application of event cameras in complex scenarios. Previous works for ESR often process positive and negative events in a mixed paradigm. This paradigm limits their ability to effectively model the unique characteristics of each event and mutually refine each other by considering their correlations. In this paper, we propose a bilateral event mining and complementary network (BMCNet) to fully leverage the potential of each event and capture the shared information to complement each other simultaneously. Specifically, we resort to a two-stream network to accomplish comprehensive mining of each type of events individually. To facilitate the exchange of information between two streams, we propose a bilateral information exchange (BIE) module. This module is layer-wisely embedded between two streams, enabling the effective propagation of hierarchical global information while alleviating the impact of invalid information brought by inherent characteristics of events. The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods in ESR, achieving performance improvements of over 11\% on both real and synthetic datasets. Moreover, our method significantly enhances the performance of event-based downstream tasks such as object recognition and video reconstruction. Our code is available at https://github.com/Lqm26/BMCNet-ESR.

翻译：事件流超分辨率（Event Stream Super-Resolution, ESR）旨在解决事件流空间分辨率不足的问题，这对事件相机在复杂场景中的应用具有重要意义。以往ESR方法常采用混合范式处理正负事件，但这种范式限制了模型对每种事件独特特征的有效建模，以及通过考虑事件间相关性实现相互优化的能力。本文提出一种双边事件挖掘与互补网络（BMCNet），以充分挖掘每种事件的潜在信息，同时捕获共享信息实现相互补充。具体而言，我们通过双流网络分别对每类事件进行全方位挖掘。为促进双流间的信息交互，我们设计了双边信息交换模块（Bilateral Information Exchange, BIE）。该模块以分层方式嵌入双流之间，既能有效传播层级全局信息，又能减轻事件固有特征带来的无效信息影响。实验结果表明，本方法在ESR任务上超越先前最先进方法，在真实和合成数据集上性能提升均超过11%。此外，本方法显著增强了基于事件的下游任务（如目标识别与视频重建）的性能。代码已开源至 https://github.com/Lqm26/BMCNet-ESR。