Current Event Stream Super-Resolution (ESR) methods overlook the redundant and complementary information present in positive and negative events within the event stream, employing a direct mixing approach for super-resolution, which may lead to detail loss and inefficiency. To address these issues, we propose an efficient Recursive Multi-Branch Information Fusion Network (RMFNet) that separates positive and negative events for complementary information extraction, followed by mutual supplementation and refinement. Particularly, we introduce Feature Fusion Modules (FFM) and Feature Exchange Modules (FEM). FFM is designed for the fusion of contextual information within neighboring event streams, leveraging the coupling relationship between positive and negative events to alleviate the misleading of noises in the respective branches. FEM efficiently promotes the fusion and exchange of information between positive and negative branches, enabling superior local information enhancement and global information complementation. Experimental results demonstrate that our approach achieves over 17% and 31% improvement on synthetic and real datasets, accompanied by a 2.3X acceleration. Furthermore, we evaluate our method on two downstream event-driven applications, \emph{i.e.}, object recognition and video reconstruction, achieving remarkable results that outperform existing methods. Our code and Supplementary Material are available at https://github.com/Lqm26/RMFNet.

翻译：当前的事件流超分辨率方法忽视了事件流中正负事件间存在的冗余与互补信息，采用直接混合的方式进行超分辨率处理，可能导致细节丢失与效率低下。为解决这些问题，我们提出了一种高效的递归多分支信息融合网络，该网络通过分离正负事件以提取互补信息，继而进行相互补充与精细化处理。具体而言，我们引入了特征融合模块与特征交换模块。特征融合模块专为融合相邻事件流中的上下文信息而设计，利用正负事件间的耦合关系以减轻各分支中噪声的误导性影响。特征交换模块有效促进了正负分支间的信息融合与交换，实现了更优的局部信息增强与全局信息互补。实验结果表明，我们的方法在合成数据集与真实数据集上分别取得了超过17%与31%的性能提升，同时实现了2.3倍的加速。此外，我们在两个下游事件驱动应用（即目标识别与视频重建）上评估了所提方法，取得了超越现有方法的显著成果。我们的代码与补充材料已公开于https://github.com/Lqm26/RMFNet。