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。

高效的事件流超分辨率与递归多支融合

Efficient Event Stream Super-Resolution with Recursive Multi-Branch  Fusion

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 this https URL

本研究提出了一种双边事件挖掘和互补网络（BMCNet），旨在完全发挥每个事件的潜力，同时捕捉共享信息以相互补充，通过两流网络实现对每种类型事件的全面挖掘，并通过双边信息交换（BIE）模块促进两个流之间的信息交流，在事件的固有特性带来的无效信息的影响下减轻有效信息的传播，实验证明我们的方法在事件流超分辨率方面优于现有方法，并在真实数据集和合成数据集上分别实现了超过 11％的性能提升，此外，我们的方法显著改善了基于事件的下游任务的性能，如目标识别和视频重构。