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
this https URL

我们提出了一种高效的递归多分支信息融合网络（RMFNet），用于处理事件流中的正负事件，实现互补信息提取和改善效果。经实验证实，我们的方法在合成和真实数据集上分别取得了 17% 和 31% 的改进，并且加速了 2.3 倍。此外，我们还在物体识别和视频重建等应用中验证了我们的方法，取得了优秀的结果。