README.md

CasFormer: Cascaded Transformers for Fusion-aware Computational Hyperspectral Imaging

Chenyu Li, Bing Zhang, Danfeng Hong, Jun Zhou, Gemine Vivone, Shutao Li, Jocelyn Chanussot

**Information Fusion: (https://doi.org/10.1016/j.inffus.2024.102408).

Fig.1. The overall architecture of CasFormer with the input of RGB, coded measurement, and mask. The core module of CasFormer consists of a series of cascade-attention (CA) blocks, where “mask” is directly correlated with imaging devices.

Code Running

💢 Before: For the required packages, please refer to detailed .py files;

🚀 Parameters: The trade-off parameters as test_code/option.py* could be better tuned and the network hyperparameters are flexible;

🛰️ Results: Please see the three evaluation metrics (PSNR, SSIM, and SAM);

🌍 The experiments were run on 8 NVIDIA GeForce RTX 3090 GPUs.

Note

• You may need to manually download the CAVE dataset (http://www.cs.columbia.edu/CAVE/databases/multispectral), KAIST dataset (https://vclab.kaist.ac.kr/siggraphasia2017p1/kaistdataset.html), and ICVL dataset (http://icvl.cs.bgu.ac.il/hyperspectral/).

• The pretrained model (test_code/model_zoo/cave_model.pth) can be downloaded from Baidu Disk with code: d5yc or Google Drive.

• Simply run test_code/test.py to reproduce our method on CAVE test dataset from Baidu Disk with code: cabi or Google Drive.

|--datasets
        |-- Test
               |-- cave_test
                        |--scene1.mat
                        |--scene2.mat
                        ：
               ：
        |-- Train
               |-- cave_train
                        |--scene1.mat
                        |--scene2.mat
                        ：
               |-- mask_train
               ：

|-- test_code
        |--model_zoo
             |--cave_model.pth
        |--result
        |--option.py
        |--test.py
        ：

|-- train_code
        |--exp
        |--option.py
        |--train.py
        ：                 

Citation Details

Please kindly cite the papers if this code is useful and helpful for your research.

@article{li2024casformer,
  title={CasFormer: Cascaded transformers for fusion-aware computational hyperspectral imaging},
  author={Li, Chenyu and Zhang, Bing and Hong, Danfeng and Zhou, Jun and Vivone, Gemine and Li, Shutao and Chanussot, Jocelyn},
  journal={Information Fusion},
  pages={102408},
  year={2024},
  publisher={Elsevier}
}

Licensing

Copyright (C) 2024 Danfeng Hong

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program.

Contact Information:

Danfeng Hong: hongdanfeng1989@gmail.com
Danfeng Hong is with the Aerospace Information Research Institute, Chinese Academy of Sciences, 100094 Beijing, China.