## 4 Evaluation Method  
It is difficult to obtain target information from a single event in the event image of the DVS, and it is difficult to distinguish whether it is a real event or noise after imaging. When the trajectory of the target is known, the time correlation of events generated by a single pixel can be counted through repeated recording. The higher the correlation, the higher the probability of real events, and the lower the probability of noise. However, in natural scenes, the target's motion information cannot be obtained, and relying on this method of judging image quality is not feasible. According to the spatiotemporal correlation of events, the average time and space between real events is smaller than that of the noise. A single event contains very little information, and the probability that an event is a signal event or noise requires a period of event flow to calculate. First calculate the spatial distance between the event and other events, reduce the dimension of the parameter, and use the event \(e_{0}(x_{0},y_{0},t_{0})\) as the \"center\" to express the event sequence, that is  
\[ES(d,t)\ =\sum_{i=1}^{N}\delta(d-\Delta d_{i},t-\Delta t_{i}), \tag{12}\]  
where \(\delta(d,t)\) is the pulse function, N is the number of events,  
\[\Delta d_{i}\ =\ [(x_{i}-x_{0})+(y_{i}-y_{0})]^{\frac{1}{2}},\ \text{and} \tag{13}\]  
\[\Delta t_{i}\ =\ \big{|}\big{(}t_{i}-t_{0}\big{)}\big{|}. \tag{14}\]  
Then use a two-dimensional Gaussian kernel  
\[G(d,t)=\frac{1}{2\pi\sigma_{1}\sigma_{2}}e^{-\frac{1}{2}(\frac{d_{i}^{2}}{ \sigma_{1}^{2}}+\frac{d_{i}^{2}}{\sigma_{2}^{2}})} \tag{15}\]  
to convolve with the above event sequence to get the real event probability of the event  
\[P_{e_{0}}=ES(d,t)\otimes G(d,t), \tag{16}\]  
namely  
\[P_{e_{0}}=\sum_{i=1}^{N}\frac{1}{2\pi\sigma_{1}\sigma_{2}}e^{-\frac{1}{2}( \frac{(\Delta d_{i})^{2}}{\sigma_{1}^{2}}+\frac{(\Delta t_{i})^{2}}{\sigma_{2} ^{2}})}. \tag{17}\]  
Among them, \(\sigma_{1}\) and \(\sigma_{2}\) are the standard deviations of the spatial distance \(d\) and the time distance \(t\), respectively, which are determined by the resolution of the DVS and the time interval of the event stream. The location and time of the event are independent of each other, so \(\rho=0\) in the normal distribution. It can be seen from the formula that if there is only one hot pixel noise in the image, then the real event probability of these noises will be abnormally high. In order to eliminate this effect, the real event probability when \(d=0\) is set artificially as 0, namely  
\[P_{e_{0}}=\left\{ \begin{array}{ll}\sum\limits_{i=1}^{N}\frac{1}{2\pi\sigma_ {1}\sigma_{2}}e^{-\frac{1}{2}(\frac{(\Delta d_{i})^{2}}{\sigma_{1}^{2}}+\frac{ (\Delta t_{i})^{2}}{\sigma_{2}^{2}})}&d\
eq 0\\ 0&d=0\end{array} \right.. \tag{18}\]  
For the evaluation of image quality, it is necessary to calculate the number of events with low real events probability in the image. The criterion for low real events probability is less than or equal to BAs' average real events probability. BA will change with factors such as light intensity, ON/OFF threshold, sensor temperature, etc. Therefore, the selection of BA is performed by manually opening the window in the original event stream, that is, selecting some pixels in the area without targetmovement, counting the real event probability of each BA, and then calculating the average real event probability of the BA, \(P_{ARE}\). Comparing the passed events with it, the fewer events less than the BAs' average real event probability, namely noise in real (NIR), the less residual noise after filtering. NIR is calculated from Equation (19), namely  
\[NIR=\sum\delta(x-x_{i},y-y_{i},t-t_{i})\ \ \ \ \ e_{i}(x_{i},y_{i},t_{i})\in \Lambda_{PE}\ \ and\ \ P_{e_{i}}\leq P_{ARE}, \tag{19}\]  
where \(\Lambda_{PE}\) is a collection of passed events in the period. Comparing the filtered noise events with it, the fewer events higher than the BA average true event probability, namely real in noise (RIN), the fewer the filtered real events.