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CSST--Reconstruction-Algorithm-of-Aperture-Diffraction-Imaging-Spectrometer

Content Alerts

  • More general experimental results under different exposure conditions are that the CSST-9stg can improve close to 1.5 dB in PSNR over the Restormer on the task of ADIS reconstruction.
  • Notably, the algorithm can be used to solve the inverse problem of PSF engineering and RGB super-resolution.
  • This toolkit will be continually refined.

Acknowledgements

  • We would like to express our gratitude to the author and contributors of MST-plus-plus and MST for their valuable work.
  • CSST is an open-source project that leverages the functionality provided by external libraries called MST-plus-plus and MST.
  • CSST modifies the underlying framework from MST-plus-plus and MST to perform a completely noval reconstruction task (ADIS) as well as PSF engineering. You can find the original MST-plus-plus repository here and MST repository here.

1. Result:

A more general result is that the CSST-9stg exhibits around 35dB in PSNR.

 

2. Create Environment:

  • Python 3 (Recommend to use Anaconda)

  • NVIDIA GPU + CUDA

  • Python packages:

  pip install -r requirements.txt

 

3. Prepare Dataset:

|--CSST
    |--Real
    	|-- test_code
    	|-- train_code
    |--simulation
    	|-- train_code
    |--tools
    |--datasets
        |--cave_1024_28
            |--scene1.mat
            |--scene2.mat
            ：  
            |--scene205.mat
        |--KAIST_CVPR2021_selected 
            |--1.mat
            |--2.mat
            ： 
            |--20.mat
        |--KAIST_CVPR2021_unselected 
            |--1.mat
            |--2.mat
            ： 
            |--10.mat

we use the CAVE dataset (cave_1024_28) and KAIST (KAIST_CVPR2021_selected) (10 secnes of KAIST_CVPR2021_unselected are croped as testset) as the simulation training set. Both the CAVE (CAVE_512_28) and KAIST (KAIST_CVPR2021) datasets are used as the real training set.

We will soon make available the training and test sets used in the paper implementation.

 

4. Simulation Experiement:

4.1　Training

python train.py --template CSST-3stg --outf ./exp/CSST-3stg/ --method CSST-3stg
python train.py --template CSST-5stg --outf ./exp/CSST-5stg/ --method CSST-5stg
python train.py --template CSST-7stg --outf ./exp/CSST-7stg/ --method CSST-7stg
python train.py --template CSST-9stg --outf ./exp/CSST-9stg/ --method CSST-9stg

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

4.2　Testing

By loading model weights in such a way that you can quickly implement model testing from the training code

4.3 Evaluating the Params and FLOPS of models

We have provided a function my_summary() in simulation/train_code/utils.py, please use this function to evaluate the parameters and computational complexity of the models, especially the Transformers as

from utils import my_summary
my_summary(CSST(), 256, 256, 28, 1)

 

5. Real Experiement:

5.1　Training

python train.py --template CSST-3stg --outf ./exp/CSST-3stg/ --method CSST-3stg
python train.py --template CSST-5stg --outf ./exp/CSST-5stg/ --method CSST-5stg
python train.py --template CSST-7stg --outf ./exp/CSST-7stg/ --method CSST-7stg
python train.py --template CSST-9stg --outf ./exp/CSST-9stg/ --method CSST-9stg

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

5.2　Testing

# first step
cd CSST/Real/test_code
python test_CSST_final.py

 

6. Citation

If this repo helps you, please consider citing our works:

# ADIS+CSST
@inproceedings{lv2023aperture,
  title={Aperture Diffraction for Compact Snapshot Spectral Imaging},
  author={Lv, Tao and Ye, Hao and Yuan, Quan and Shi, Zhan and Wang, Yibo and Wang, Shuming and Cao, Xun},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={10574--10584},
  year={2023}
}