# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 4 Results
### Comparative Experiments  
In order to evaluate the reconstruction quality, we introduce two quantitative metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) [46]. Higher values for both metrics indicate better reconstruction quality. We compare the proposed DualCT-Net with four state-of-the-art deep network-based reconstruction methods: TSA-Net [29], SRN [47], GST [48], and MST [25]. A small variant of the MST model is used in our experiments and we denote it as MST-S. Table 1 shows the PSNR and SSIM values of the five reconstruction methods across ten scenes.  
As can be seen from Table 1, our DualCT-Net has the best reconstruction results upon all 10 test scenes. TSA-Net [29], SRN [47], and GST [48] are CNN-based reconstruction methods, and MST-S [25] is a Transformer-based reconstruction method. Our network surpasses comparative methods by a large margin. More specifically, the average PSNR value of the DualCT-Net model is 4.34 dB, 3.55 dB, 2.79 dB, and 1.54 dB higher than the state-of-the-art deep network-based methods TSA-Net, SRN, GST, and MST-S, respectively. These results verify the effectiveness of configuring the CNN + Transformer in the DualCT module. The DualCT-Net can effectively integrate the advantages of CNN and the Transformer, capturing spatial-spectral features of hyperspectral images. Figures 6 and 7 visualize spectral bands reconstructed by five methods and the ground-truth spectral bands. We also zoom in on some areas for comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **TSA-Net** & **SRN** & **GST** & **MST-S** & **Ours** \\ \hline Scene 1 & 32.03/0.892 & 33.26/0.910 & 33.99/0.926 & 34.71/0.930 & 36.01/0.948 \\ Scene 2 & 31.00/0.858 & 29.86/0.881 & 30.49/0.900 & 34.45/0.925 & 37.21/0.953 \\ Scene 3 & 32.25/0.915 & 31.69/0.909 & 32.63/0.921 & 35.32/0.943 & 37.66/0.959 \\ Scene 4 & 39.19/0.953 & 39.90/0.947 & 41.04/0.967 & 41.50/0.967 & 43.71/0.985 \\ Scene 5 & 29.39/0.884 & 30.86/0.923 & 31.49/0.938 & 31.90/0.933 & 33.21/0.958 \\ Scene 6 & 31.44/0.908 & 34.20/0.941 & 34.89/0.955 & 33.85/0.943 & 34.95/0.959 \\ Scene 7 & 30.32/0.878 & 27.27/0.852 & 27.63/0.866 & 32.69/0.911 & 34.23/0.943 \\ Scene 8 & 29.35/0.888 & 32.35/0.932 & 33.02/0.947 & 31.69/0.933 & 32.94/0.954 \\ Scene 9 & 30.01/0.890 & 32.83/0.921 & 33.45/0.932 & 34.67/0.939 & 35.70/0.952 \\ Scene 10 & 29.59/0.874 & 30.25/0.905 & 31.49/0.935 & 31.82/0.926 & 32.40/0.942 \\ Average & 31.46/0.894 & 32.25/0.912 & 33.01/0.929 & 34.26/0.935 & 35.80/0.955 \\ \hline \end{tabular}
\end{table}
Table 1: PSNR and SSIM values of reconstruction results of 5 methods across 10 scenes.  
Figure 6: Visualization of three spectral bands reconstructed by five methods in scene 2.  
It can be clearly seen that the reconstructed images of TSA-Net can recover the outline of the object, but produce blurry, distorted, and incomplete feature maps. Reconstructed images of SRN and GST are prone to having artifacts along the object contours. Compared with the MST-S method, the DualCT-Net network can reconstruct more clear details.  
In addition, Figures 8 and 9 show reconstructed spectral signatures corresponding to annotated rectangular regions in the RGB images. Correlation coefficients are also calculated to quantitatively evaluate the fidelity of reconstructed spectral signatures. Spectral signatures reconstructed by our method can adequately match the reference signatures and have spectral fidelity.  
Figure 8: Reconstructed spectral signatures by five methods of the reconstructed hyperspectral image of scene 2.  
Figure 7: Visualization of three spectral bands reconstructed by five methods in scene 8.