## 4 Experiment Results  
### Experimental Setting  
Our CFSR framework was implemented by the publicly available Matlab (version 2019a), Python (version 2.7), and Brian2 (version 2.2.1) on an AMD Ryzen 7 5800H-GPU system. To demonstrate the CFSR framework, we used the PKU-SPIKE-RECON dataset [6; 8] from Peking University, including spike sequences captured by the spike camera. This dataset contains eight sequences including two categories of normal speed (Class A) and high speed (Class B) scenarios. Each sequence was captured by the spike camera with 40,000 Hz sampling rate. The experiments were conducted on three spike sequences captured from high-speed scenes, including \"Rotation1\", \"Rotation2\" and \"Car\", as shown in Figure 4. Among them, \"Car\" describes a car traveling at a speed of 100 km/h (kilometers per hour). The sequence \"Rotation1\" describes a disk with 2000 rpm (revolutions per minute), and the sequence \"Rotation2\" depicts an electric fan with 2600 rpm [18]. The resolution of these three high-speed scenes is of size 400 \(\times\) 250 \(\times\) 2000. According to our previous statistics, we set \(T\) = 400, \(\theta\) = 6, \(\varphi_{0}\) = 50, \(\varphi\) = 4, and \(\varnothing\) = 12, respectively. To evaluate the performance of our CFSR method on reconstructing high-speed moving scenes, we compared it with three conventional texture reconstruction methods, including \"Texture from ISI (TFI)\", \"Texture from Playback (TFP)\" (w = 160), and a spiking neural model [8]. Figure 5: Comparison of the coarse-grained and fine-grained distinctions with raw spike and the TFI result. From left to right: (**a**) raw spike; (**b**) the reconstruction by the TFI; (**c**) the coarse-grained distinction; and (**d**) the fine-grained distinction. The white and black value in both (**c,d**) represent the dynamic and static regions, respectively. Figure 6: Visual quality comparison of texture reconstruction by the TFP, the TFI, and the spiking neural model and our proposed method. From left to right: (**a**) TFP (\(w=160\)); (**b**) TFI; (**c**) spiking neural model; (**d**) coarse-grained reconstruction; (**e**) fine-grained reconstruction; (**f**): closeups of the reconstructed results on each method, respectively. **Quantitative Evaluation. ** We also evaluated our proposed CFSR using an image quality assessment metric standard deviation (STD) and a no-reference image blur metric called CPBD [34] on the consecutive 200 frame raw spikes from the PKU-SPIRE-RECON dataset for quantitative comparison. The STD is related to the contrast of the image; it can be computed as \(STD=\sqrt{\frac{\sum_{i=1}^{H}\sum_{y=1}^{W}\left(I_{(x,y)}-I\right)^{2}}{\left(H -1\right)\left(W-1\right)}}\), where \(H\) and \(W\) represent the width and length of the reconstructed image, \(I_{(x,y)}\) is the gray value of the pixel \((x,y)\), and \(\bar{I}\) is the sample mean. Larger standard deviation (STD) values mean higher contrast. CPBD was used to measure the motion blur. From the probability density function of detecting blur, the cumulative probability of blur detection was calculated as \(CPBD=\sum_{P_{BLUR}=0}^{P_{BLUR}=P_{INB}}P(P_{BLUR})\), where \(P_{BLUR}=1-exp(-\left|\frac{w(e_{i})}{w_{NB}(e_{i})}\right|^{\beta})\), \(w(e_{i})\) is the measured width of the each edge \(e_{i}\), \(w_{INB}(e_{i})\) is the \"just noticeable blur\" (NB) [35] width, which depends on the local contrast \(C\) in the neighborhood of edge \(e_{i}\), and \(\beta\) is a parameter whose value is obtained by means of least squares fitting. \(P(P_{BLUR})\) denotes the value of the probability distribution function at a given \(P_{BLUR}\). At the JNB, \(w(e_{i})=w_{INB}(e_{i})\), which corresponds to the probability of blur detection \(P_{BLUR}=P_{JNB}=63\%\). Higher CPBD means less blur and vice versa. As listed in Table 1, our CSFR outperformed other state-of-the-art methods in terms of STD. Compared to the SNM method, our CFSR model had a 22.61% improvement in terms of STD on average.