# Transformer-Based Cascading Reconstruction Network for Video Snapshot Compressive Imaging
## 5 Experiment Results and Analysis
### Results on Simulation Datasets  
In this section, we compare the proposed network with several representative methods, including the iterative optimization-based methods GAP-TV [13], GMM-TP [11], MMLE-MFA [9] and MMLE-GMM [9] and four deep learning-based methods, including the plug-and-play methods PnP-FFDNet [17], E2E-CNN [18], RE\({}^{2}\)-Net [19] and [PERSON]'s algorithm [30]. We use the peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) [38] as metrics to evaluate the reconstruction quality. The PSNR and SSIM of each method are listed in Table 1. The GAP-TV method uses total variation as the prior regularization, and its PSNR and SSIM values are the lowest. Both GMM-TP and MMLE-GMM use the Gaussian mixture model as the prior regularization. The performance of MMLE-GMM is better than that of GMM-TP. MMLE-MFA is a simplified model of MMLE-GMM, and its reconstruction performance is lower than that of MMLE-GMM. PnP-FFDNet, E2E-CNN, [PERSON]'s algorithm and RE\({}^{2}\)-Net are deeplearning-based methods, and their reconstruction qualities are generally higher than the traditional iterative optimization-based methods. RE\({}^{2}\)-Net learns the spatiotemporal correlation through network ensemble and has the second-best results. [PERSON]'s algorithm uses Transformer to capture long-range spatiotemporal features and has the best reconstruction on the Kobe and Aerial datasets. However, the average results of [PERSON]'s algorithm are lower than those in our network. The proposed network has the optimal reconstruction quality in Runner and Drop and achieves the optimal average PSNR and SSIM values among all the methods. Figure 5 shows the reconstruction results of two stages in our network, including the overall structure reconstruction, the incremental details reconstruction and the final reconstruction. It can be seen that the second stage can reconstruct informative incremental details and further enhance the quality of the final reconstruction, which verifies the rationality of our cascading network design. The advantages of our network mainly lie in three aspects. First, the proposed network adopts the multi-scale Transformer structure to fully extract the overall spatial and temporal correlation of video frames. Second, according to the residual measurement mechanism, we decompose the reconstruction as the cascading of the overall structure reconstruction and the incremental details reconstruction, which helps to reduce the learning difficulty of the network. Lastly, the dynamic feature fusion module can adaptively fuse the reconstruction of two stages and reconstruct more incremental details. Figure 6 shows the reconstructed video frames of different methods on the simulation datasets. The leftmost column is Ground Truth, and the rightmost is the result of our network. It can be seen from Figure 5 that the video frames reconstructed by GMM-TP, MMLE-MFA and MMLE-GMM have a relatively blurry contour, and there are artifacts in the reconstruction. PnP-FFDNet, E2E-CNN and RE\({}^{2}\)-Net make some improvements on the contour reconstruction, but they still lack structural details in reconstruction frames. The