## 3 The CFSR Method
### Coarse-Grained Reconstruction  
For the input raw spike data, we first use the TFI method for global reconstruction and distinguish the spikes in a coarse-grained level. We exploit the spatial distribution for ISI to guide the coarse-grained distinction process. As shown in Figure 2a, we observe that there are different numbers and values of ISI within a fixed time interval \(T\), which is formulated as a set,  
\[T=\ \left\{\Delta t_{m}\right\}_{m=1}^{n}, \tag{5}\]  
where \(n\) denotes the number of ISI in the set \(T\), and \(\Delta t_{m}\) is the m-th inter-spike interval in the fixed time interval \(T\).  
Given a fixed time interval \(T\), the cardinality of the set \(T\) are not identical. As shown in Figure 2b, when the pixel belongs to the static region, each ISI value in the \(\left\{\Delta t_{m}\right\}_{m=1}^{n}\) is relatively average. However, the differences of all the ISI values \(\left\{\Delta t_{m}\right\}_{m=1}^{n}\) for the dynamic region are large.Thus, the cardinality of the set \(T\) in the dynamic region is larger than that in the static region. Therefore, we distinguish the dynamic and static regions coarsely by  
\[M_{t_{m}}(x,y)=\begin{cases}1&\quad\left|\left\{\Delta t_{m}\right\}_{m=1}^{n} \right|\geq\theta\\ 0&\quad\left|\left\{\Delta t_{m}\right\}_{m=1}^{n}\right|<\theta,\end{cases} \tag{6}\]  
where \(\theta\) represents the predetermined threshold, and \(\left|\bullet\right|\) is the cardinality of a given set. \(M_{t_{m}}\in\left\{0,1\right\}\) is a binary matrix denoting the states of the spike, where the value 1 denotes that the pixel (x, y) belongs to the dynamic region at moment \(t_{m}\). The real-time spike can be marked as static and dynamic states adaptively by Equation (6). In addition, the range of \(\theta\) depends on \(n\), and the size of \(n\) is related to time and light intensity. We empirically set the threshold \(\theta\).  
Figure 1: The overall architecture of the spike-based CFSR framework. The coarse-grained reconstruction branch is denoted by dark blue lines. The input is spike data and the outputs are spike train with binary marks (dynamic or static) and the CSR, which are the inputs of the fine-level reconstruction branch denoted by the red lines. The image with the blue wire frame is the intermediate result fused by the results of the TFP and TFI methods directly.  
For the dynamic and static regions after coarse-grained spike state distinction, we use different characteristics of the static and dynamic regions for image reconstruction. The TFI method (3) and the TFP method (4) mentioned in Section 2.2 are utilized for the dynamic region reconstruction and static region reconstruction, respectively, as shown in Figure 1.  
After the static and dynamic region reconstruction, to keep the consistency of brightness of the two states reconstruction we adopt an adaptive threshold scene reconstruction (ATSR) step to fuse the static and dynamic region reconstruction. We calculate the average brightness ratio of the reconstructions of static regions via the TFP and TFI methods as the adaptive threshold to adjust the brightness of the static region reconstruction via the TFP method,  
\[S^{{}^{\prime}}_{TFP}(x,y)=\ S_{TFP}(x,y)\cdot\sum_{(x,y)\in R}\left(S_{TFI}(x,y)/S_{TFP}(x,y)\right)/N, \tag{7}\]  
where \(R\) represents the static region of the reconstruction image, \(S\) is the value of static region of the reconstruction, and \(S_{TFP}\) represents the value of the static region of the reconstruction by the TFP method. \(N\) is the number of spikes in the region \(R\).  
Here, the scene reconstruction of static regions is also performed by the TFI method as a guide to obtain the adaptive threshold. After multiplying by the adaptive threshold, the brightness value of the static region via the TFP method is adjusted to be as consistent as possible with that of the dynamic region. Thus, we can obtain the fused scene. In addition, the adaptive threshold is also adopted in the ATSR of the fined-grained reconstruction.