# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 6 Conclusions  
In this paper, we aim to integrate the advantages of CNN and Transformer for representing multi-scale coupling correlations within hyperspectral images. Specifically, we propose a dual-branch CNN-Transformer complementary module. It combines the Transformer self-attention mechanism with deep convolution through dual branches and bidirectional cross-branch interactions. Therefore, it can learn complementary features in both spectral and spatial dimensions. In addition, according to the compressive snapshot imaging mode, this paper also introduces a mask guidance mechanism to refine the spatial-spectral structure of the reconstructed image. By using the DualCT module as a basic component, this paper further designs a multi-scale encoding and decoding model to learn the end-to-end reconstruction mapping from snapshot measurements to the original hyperspectral images. Quantitative experimental results have verified the effectiveness of our network design. It is hoped that the network design and potential insights proposed in this paper will contribute to future work in emerging SCI research. Conceptualization: Y.S. ; data curation: K.H. and Q.G. ; methodology: K.H. ; software: K.H. ; supervision: [PERSON]; writing--original draft: [PERSON]; writing--review and editing: [PERSON]. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China under grants 62276139 and U2001211. Not applicable. The data presented in this study can be available on request from the corresponding author. The data are not publicly available due to privacy. The authors would like to thank the people who shared data and basic models from their research with the community. Their selflessness significantly drives the research process for the entire community. In addition, we sincerely express our gratitude to [PERSON] for her assistance with the experiments and valuable discussions. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. * _[PERSON] et al. (2014)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Spatial-spectral encoded compressive hyperspectral imaging. _ACM Trans. Graph._**2014**, _33_, 1-11. [CrossRef]
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