Denoising algorithm based on event camera

The event camera is a novel type of bio-inspired vision sensor inspired by the biological retina. Compared to traditional frame-based cameras, it offers high temporal resolution, high dynamic range, reduced redundancy, and lower transmission bandwidth. These unique features pave the way for innovative solutions in the field of computer vision. However, the heightened sensitivity of event cameras to fluctuations in brightness, along with their susceptibility to environmental factors and hardware limitations, presents a significant challenge. It involves capturing spatiotemporal information from the target signal simultaneously with the generation of a substantial volume of noise events. In applications relying on event cameras, this noise compromises target detection precision. Therefore, event stream denoising is essential before further applications can be pursued. Unfortunately, conventional frame-based algorithms are ill-suited for processing event data due to the distinct format of event cameras. In response to the challenges of event stream denoising, using the event stream generated by Celex-V as an example, this paper categorizes noise events and conducts an analysis of the event noise distribution model. Leveraging the characteristics of noise events, such as randomness and isolation, the paper proposes an event-based cascaded noise processing method. This method involves analyzing events in the spatiotemporal vicinity of arriving events and removing noise events from the event stream data. While ensuring the integrity of data flow information, it achieves rapid and efficient noise removal. The denoised event stream is advantageous for subsequent processing in various applications based on event cameras.