Pytorch implementation of "Multi-Modal Spectral Image Super-Resolution" for PIRM2018 Challenge
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README.md

README.md

Multi-Modal Spectral Image Super-Resolution

Code

Dependencies

  • Pytorch 0.4.0
  • cuDNN

Our code is tested under Ubuntu 14.04 environment with Titan X GPUs.

Inference for Track1

  1. goto folder: data/track1/
  2. run download_testing_data.sh
  3. run generate_testing_h5.m
  4. goto folder: code/track1/
  5. run: python validate.py
  6. run: python npz2mat.py
  7. run mat2fla.m

the reconstruction is in code/track1/validation/*.{hdr,fla}

Inference for Track2

  1. goto folder: data/track2/
  2. run download_testing_data.sh
  3. run generate_testing_h5.m
  4. goto folder: code/track2/
  5. run: python validate.py
  6. run: python npz2mat.py
  7. run mat2fla.m

the reconstruction is in code/track2/validation/*.{hdr,fla}

Training for Stage-I (Track1):

  1. goto folder: data/track1/
  2. run download_training_data.sh
  3. run generate_training_h5.m
  4. goto folder: code/track1/
  5. run: cp -r ../../data/track1/hd5 ./data
  6. run: python main.py

Training for Stage-II (Track2):

  1. train Stage-I
  2. goto folder: data/track2/
  3. run download_training_data.sh
  4. run generate_stage_one_h5.m
  5. run: python generate_stage_one_results.py
  6. run: python npz2mat.py
  7. run mat2flat.m
  8. run generate_training_h5.m
  9. goto folder: code/track2/
  10. run: cp -r ../../data/track2/hd5 ./data
  11. run: python main.py

Authors

License

  • For academic and non-commercial use only.