# Transformer-Based Cascading Reconstruction Network for Video Snapshot Compressive Imaging
## 5 Experiment Results and Analysis
### Ablation Studies  
In this section, we take a series of ablation experiments to understand the impact of the performance of the different parts of the proposed network. We design ablation methods by removing some modules from the proposed network. The ablation methods 1-3 are to verify the effectiveness of the residual measurement mechanism and dynamic feature fusion module. The ablation methods 4-7 modify the group number of the multi-scale structure in the overall structure reconstruction network and the number of RWT modules in the incremental details reconstruction stage. Table 2 demonstrates the detailed setting of seven ablation methods and their experimental results on the simulation datasets.  
In ablation methods 2 and 3, the removing of the residual measurement mechanism means that we directly take the output of the overall structure reconstruction network as the input of the incremental details network. In ablation methods 1 and 3, the removing of the dynamic feature fusion module means that the second stage only takes the output of the first stage as the input instead of fusing it with the deep feature captured by the first stage. In these three experiments, one or two modules are removed, respectively, and the rest of the network maintains the original structure. As shown in Table 2, removing any module will reduce the reconstruction quality. This proves that the residual measurement mechanism and dynamic feature fusion module can make the network focus more on the recovery of details, effectively enhancing the learning ability of the network and improving the reconstruction quality.  
In ablation experiments 4-7, we change both the group number of the multi-scale structure in the overall structure reconstruction network and the number of RWT modules in the incremental details reconstruction network. In the proposed network, the overall structure reconstruction network includes three groups of multi-scale structures. As shown in Table 2, with the group number of the multi-scale structure decreasing, the PSNR is reduced from 32.42 dB to 31.16 dB, which proves the effectiveness of the RWT  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experimental** & **Residual Measurement** & **Dynamic** & **The Group Number of the** & & & \\
**Setting** & **Mechanism** & **Feature Fusion** & **Multi-Scale Structure** & **RWT** & **PSNR** & **SSIM** \\ \hline Ours & \(\surd\) & \(\surd\) & 3 & 3 & 32.42 & 0.949 \\ \hline Ablation 1 & \(\surd\) & \(\times\) & 3 & 3 & 31.92 & 0.935 \\ Ablation 2 & \(\times\) & \(\surd\) & 3 & 3 & 31.81 & 0.936 \\ Ablation 3 & \(\times\) & \(\times\) & 3 & 3 & 31.64 & 0.928 \\ \hline Ablation 4 & \(\surd\) & \(\surd\) & 2 & 3 & 31.54 & 0.930 \\ Ablation 5 & \(\surd\) & \(\surd\) & 1 & 3 & 31.16 & 0.923 \\ Ablation 6 & \(\surd\) & \(\surd\) & 3 & 2 & 32.29 & 0.938 \\ Ablation 7 & \(\surd\) & \(\surd\) & 3 & 1 & 32.06 & 0.927 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation results of the proposed network.  
Figure 7: Reconstruction results of different methods on the real dataset Wheel. (The red boxes are enlarged detail images.)module. When the overall structure reconstruction network is kept unchanged and the number of RWT modules in the detail reconstruction network is reduced, the quality of reconstruction will be reduced, which also proves the effectiveness of this module for the proposed network.