## 5 Experiment and Evaluation
It can be seen from Figure 11 that the method proposed in this paper has no noise in the area outside the target projection and is denser than the denoising result of [PERSON]'s filter. In the binary image of noise, the outline of the target is clearer than with [PERSON]'s filter, which shows that this method filters more real events than [PERSON]'s filter, but compared with the other two filters, the real events are better retained. The \(\sigma_{1}\) and \(\sigma_{2}\) parameters in the evaluation model are set according to the chip resolution and time interval size. If they are set too small, the real event probability of real events will also approach 0. If they are set too large, then the real events probability of events at the edge of space-time will be lower than the events in the center of space-time. The subfigures in Figure 12 are heat maps of evaluation models with different \(\sigma_{1}\) and \(\sigma_{2}\). The abscissa is the value of d and the ordinate is the value of t. The color corresponds to the weight of the evaluation model. The dark color indicates a larger value, and the light color indicates a smaller value. The heat map shows the change trend of the weight of the evaluation model in time and space by color. As can be seen from Figure 12, when \(\sigma_{1}\) and \(\sigma_{2}\) are both 2, the events at the edge of time and space will not be affected. As a result, the evaluation model parameters are determined accordingly. Figure 4.1 Binary images of the raw image and the events filtered by the five filters at 3923 ms. Calculate NIR of results and RIN of the filtered noise at 3923 ms of each filter respectively, as shown in Figure 4.2 Both \(\sigma_{1}\) and \(\sigma_{2}\) are 2, they are used in the Gaussian kernel, and the average real event probability of the noise at this moment is 0.0007768. Figure 4.3 Heat maps of evaluation models with different \(\sigma_{1}\) and \(\sigma_{2}\). Figure 4.4 _Cont._  
The average real event probability of noise over 288 time periods is 0.001. The Figures 14 and 15 show the comparison of noise in real and real in noise of each filter. It can be seen that the method in this paper is the best of the four methods from the figure of noise in real. This is because the noise determination in this method is based on the density of events, using multiple events to support the identified event to get more accurate results. From the comparison chart of real in noise, it is seen that the value of [PERSON]'s filter is the lowest. The method of this article and [PERSON]'s filter's indicators are intertwined. Due to insufficient support events for the latest event judgment in the method described in this article, real events are filtered out, and [PERSON]'s filter caused similar results because it failed to store enough events to support the judgment. The periodic fluctuations in the two figures are due to the periodic movement of the target, the amount of data generated in different time periods is different. The peak in noise in real and the valley in real in noise correspond to the moment when the ball is at the highest point and the speed is the lowest. There are few real events at this moment and more noise, so periodic peaks and valleys appear. Figure 4.5 Noise in real (NIR) for each time period. Figure 4.6 The noise in real (**top**) and real in noise (**bottom**) of the five filters in this time period.