SST

##Create Environment:

• Python 3 (Recommend to use Anaconda)

• NVIDIA GPU + CUDA

• Python packages:

pip install -r requirements.txt

##Prepare Dataset:

Download cave_1024_28 (One Drive), CAVE_512_28 (Baidu Disk, code: ixoe | One Drive), KAIST_CVPR2021 (Baidu Disk, code: 5mmn | One Drive), TSA_simu_data (One Drive), TSA_real_data (One Drive), and then put them into the corresponding folders of datasets/ and recollect them as the following form:

|--SST
    |--real
    	|-- test_code
    	|-- train_code
    |--simulation
    	|-- test_code
    	|-- train_code
    |--visualization
    |--datasets
        |--cave_1024_28
            |--scene1.mat
            |--scene2.mat
            ：  
            |--scene205.mat
        |--CAVE_512_28
            |--scene1.mat
            |--scene2.mat
            ：  
            |--scene30.mat
        |--KAIST_CVPR2021  
            |--1.mat
            |--2.mat
            ： 
            |--30.mat
        |--TSA_simu_data  
            |--mask.mat   
            |--Truth
                |--scene01.mat
                |--scene02.mat
                ： 
                |--scene10.mat
        |--TSA_real_data  
            |--mask.mat   
            |--Measurements
                |--scene1.mat
                |--scene2.mat
                ： 
                |--scene5.mat

Following TSA-Net and DGSMP, we use the CAVE dataset (cave_1024_28) as the simulation training set. Both the CAVE (CAVE_512_28) and KAIST (KAIST_CVPR2021) datasets are used as the real training set.

##Prepare Pretrained ckpt:

Download pretrained (Simulation and real (Baidu Disk, code: 2ndf | One Drive), and then put them into the corresponding folders of pretrained/ .

Simulation Experiement:

Training

cd SST/simulation/train_code/

# SST_S
python train.py  --outf ./exp/SST_S/ --method SST_S 

# SST_M
python train.py --outf ./exp/SST_M/ --method SST_M  

# SST_L
python train.py --outf ./exp/SST_L/ --method SST_L 

# SST_LPlus
python train.py --outf ./exp/SST_LPlus/ --method SST_LPlus 

The training log, trained model, and reconstrcuted HSI will be available in SST/simulation/train_code/exp/ .

Testing

Run the following command to test the model on the simulation dataset.

cd SST/simulation/test_code/

# SST_S
python test.py  --outf ./exp/SST_S/ --method SST_S --pretrained_model_path ./SST_S.pth

# SST_M
python test.py --outf ./exp/SST_M/ --method SST_M  --pretrained_model_path ./SST_M.pth

# SST_L
python test.py --outf ./exp/SST_L/ --method SST_L --pretrained_model_path ./SST_L.pth

# SST_LPlus
python test.py --outf ./exp/SST_LPlus/ --method SST_LPlus --pretrained_model_path ./SST_LPlus.pth

• The reconstrcuted HSIs will be output into SST/simulation/test_code/exp/

Real Experiement:

Training

cd SST/real/train_code/

# SST_S
python train.py  --outf ./exp/SST_S/ --method SST_S 

# SST_M
python train.py --outf ./exp/SST_M/ --method SST_M  

The training log, trained model, and reconstrcuted HSI will be available in `SST/real/train_code/exp/` . 

Testing

cd SST/real/test_code/

# SST_S
python train.py  --outf ./exp/SST_S/ --method SST_S  --pretrained_model_path ./SST_S.pth

# SST_M
python train.py --outf ./exp/SST_M/ --method SST_M   --pretrained_model_path ./SST_M.pth

The reconstrcuted HSI will be output into `SST/real/test_code/exp/`  

Acknowledgement:

Many thanks for the excellent work that has gone before and for their dedication. The code structure and datasets are borrowed from MST and DAUHST(https://github.com/caiyuanhao1998/MST).

please consider citing their works:

@inproceedings{mst, title={Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction}, author={Yuanhao Cai and Jing Lin and Xiaowan Hu and Haoqian Wang and Xin Yuan and Yulun Zhang and Radu Timofte and Luc Van Gool}, booktitle={CVPR}, year={2022} }

@inproceedings{dauhst, title={Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging}, author={Cai, Yuanhao and Lin, Jing and Wang, Haoqian and Yuan, Xin and Ding, Henghui and Zhang, Yulun and Timofte, Radu and Van Gool, Luc}, booktitle={NeurIPS}, year={2022} }