## 5 Experiment and Evaluation  
In this section, we will use the data collected by DVS to compare the performance of the filters. The filters to be compared are [PERSON]'s filter, [PERSON]'s filter, and [PERSON]'s filter. All algorithms were implemented using MATLAB. The data used was collected by the CeleX-IV imaging system. The imaging target was a swaying ball in natural light. In the experiment, the parameters used in the method of this paper are \(L=5\), \(dt=5\) ms, and the threshold is 3. In the control experiment, setup was done according to the frequency of BA in CeleX-IV and the time window used by [PERSON]'s filter. [PERSON]'s filter is set to 1 ms, and the downsampling factors of [PERSON]'s filter are 1, 2, namely time stamps from \(2\times 2\) and \(4\times 4\) pixels are stored in a cell respectively, and the time window is 1 ms. The event stream is sorted in chronological order, and the events are sent to the filter sequentially. After filtering, the passed events and filtered events are saved separately to evaluate the performance of the filter, and they are judged by the amount of noise in the passed events and the number of real events in the filtered events in the same time period. The lower the values, the better the filter performance. The spatiotemporal image of the original event stream is shown in the Figure 3. It can be seen from the figure that the BA wraps the real event in it, and the average BA frequency is calculated by sampling multiple spatiotemporal positions in the original event stream. There is no real event in the spatiotemporal positions mentioned above, only noise, which is called a noise block. The sampling position and frequency statistics are shown in the Figure 4. The dotted line in the frequency diagram is the average value, which is 0.52 Hz. Figure 3: The spatiotemporal image of the original event stream. The filtering results are shown in the Figures 5-9, where (a) of each graph is the result of denoising and (b) is the filtered noise. It can be seen from the figures that several methods can filter most of the noise, but in the space-time below the pendulum, the result of the comparison method still has some noise in it. This is because the space-time distance between BA is too small, so it was passed. However, the filter is too strict, the real events will be filtered out incorrectly, and the traces of the pendulum ball in the filtered noise will be clearer. The clearest is the result of the subsample factor of 2 in [PERSON]'s filter. The factor is smaller, and the real events of the target are stored in more cells. They cannot support each other, so they are filtered out. Figure 4: Sampled background activity (BA) events (**top**) and statistical histogram (**bottom**). Figure 5: \(Cont\). Figure 5: Filtering result of the method in this paper: (**a**) is the result of denoising and (**b**) is the filtered noise. Figure 6: Filtering result of [PERSON]’s filter: (**a**) is the result of denoising and (**b**) is the filtered noise. Figure 8: _Cont_. Figure 7: Filtering result of [PERSON]’s filter with subsample factor of 2: (**a**) is the result of denoising and (**b**) is the filtered noise. Figure 8: Filtering result of [PERSON]’s filter with subsample factor of 4: (**a**) is the result of denoising and (**b**) is the filtered noise. Figure 4.0 Filtering result of [PERSON]’s filter: (**a**) is the result of denoising and (**b**) is the filtered noise. Statistics for the entire event stream. The event stream is divided into 288 segments with a time interval of 10 ms and start time is 3553 ms. The images in Figure 10 are two-dimensional binary images of the original event stream and five results displayed at 3923 ms, and two-dimensional binary images of the filtered noise at that moment. The binary image is obtained by projecting events in the time period of 10 ms into elements at corresponding positions of a matrix of the same size as the focal plane.