## 1 Introduction  
The rapid development of imaging sensors has caused a geometric increase in the image-data quantity, rendering it difficult for the current algorithms and computing power to process such massive image data rapidly. Dynamic vision sensor (DVS) can solve the problem of large image-data quantities, from a hardware perspective [1; 2; 3]. Dynamic vision sensors are biologically inspired event-based image sensors. In the case of fast moving targets and large dynamic range, DVS can achieve low power visual sensing, but it is a challenge to traditional detectors [4].  
The pixel of DVS detects the change of light intensity. When the change reaches a certain threshold, the imaging system outputs the event coordinate, time stamp, event polarity or gray level information [5]. The output of events is asynchronous, rather than the synchronous output of traditional sensors in frame. Such characteristics make DVS more advantageous in areas such as moving target detection, simultaneous localization and mapping (SLAM), and drones [6; 7; 8; 9; 10; 11; 12; 13]. At present, commercial companies have used them for automotive and other fields [14; 15].  
However, due to thermal noise and junction leakage current, there will be output even if there is no change in light intensity [16], called background activity (BA). It can affect the image quality, waste the communication bandwidth, and consume unnecessary computing power. Therefore, a DVS denoising algorithm is necessary.  
Background activity differs from real events in that there is a higher spatiotemporal correlation between real events. In spatiotemporal images, the images of real events are denser, and BA is sparser. This difference can be used to denoise by judging the space-time density of events; the quality of the image can also be quantified by this property.  
The imaging system used in this work is CeleX-IV from CelePixel Technology Co., Ltd. [3]. The resolution of the sensor is \(768\times 640\), an Opal Kelly XEM6310 control board drives sensors and outputs data, the maximum output rate is 200 Meps, and each pixel occupies \(18\times 18\)\(\upmu\)m\({}^{2}\) with a 9% fill factor. Unlike dynamic and active-pixel vision sensor (DAVIS), the event output by this sensor does not contain polarity information, but contains light intensity information of the event [17].  
In this study, we present a method for reducing the noise in the event stream of a DVS, based on the event density in the spatiotemporal neighborhood. Our algorithm derives inspiration from the spatiotemporal principle and event-based optical flow [18; 19]. This method has two steps. The first step filters out random noise and the second step filters out hot pixels. At the same time, a method for quantifying image quality is proposed based on the spatiotemporal correlation: model a piece of event data, use a two-dimensional Gaussian kernel to convolve with each event, determine the real event probability of the noise, and evaluate the amount of noise. This method can quantify the quality of natural images and filter performance. There is no need to design fixed image stimulation in [4]. This method can evaluate the filtering effect and image quality as long as there is an image, and it is easy to implement and reproduce.  
The remainder of this paper is organized as follows: Section 2 reviews the related work on space-time correlation. Section 3 introduces the classification of noise, the concepts in the algorithm, and the design of the filter. Section 4 introduces methods for quantifying and evaluating image quality. Section 5 designs experiments to compare the denoising results of different filters. Section 6 discusses the experimental results and Section 7 presents the conclusions.