Hierarchical Regression Network for Spectral Reconstruction from RGB Images

The README file for NTIRE 2020 Spectral Reconstruction Challenge of Team OrangeCat: Hierarchical Regression Network for Spectral Reconstruction from RGB Images. Our method achieves the 1st place in track 2: real-world images.

Paper can be downloaded: https://openaccess.thecvf.com/content_CVPRW_2020/html/w31/Zhao_Hierarchical_Regression_Network_for_Spectral_Reconstruction_From_RGB_Images_CVPRW_2020_paper.html

Link to pre-trained models and testing results

Pre-trained models: OneDrive link. After downloading it, please put them to right folders.
Testing results: OneDrive link

HRNet architecture

The main network (different layers are connected by PixelShuffle and PixelUnShuffle):

The proposed ResDB and ResGB used in main network:

File structure

NTIRE 2020 Spectral Reconstruction Challenge
│   README.md
│   validation*.py
│   test*.py
│   ensemble*.py
│
└───track1 (saving the trained models of track1)
│   │   code1_G_epoch9000_bs8.pth
│   │   code1_second_G_epoch8000_bs8.pth
│   │   ...
│
└───track2 (saving the trained models of track2)
│   │   code1_bs2_G_epoch6000_bs2.pth
│   │   code2_G_epoch6000_bs8.pth
│   │   ...
|
└───NTIRE2020_Test_Clean
│    │   ARAD_HS_0468_clean.mat
│    │   ARAD_HS_0508_clean.mat
│    │   ...
│
└───NTIRE2020_Test_RealWorld
│    │   ARAD_HS_0477_RealWorld.mat
│    │   ARAD_HS_0502_RealWorld.mat
│    │   ...
│
└───test (will generate by test1.py or test2.py)
│   └───track1
│       │   ARAD_HS_0468_clean.mat
│       │   ARAD_HS_0508_clean.mat
│       │   ...
│   └───track2
│       │   ARAD_HS_0477_RealWorld.mat
│       │   ARAD_HS_0502_RealWorld.mat
│       │   ...
│
└───ensemble (will generate by ensemble1.py or ensemble2.py)
│   └───track1
│       │   ARAD_HS_0468_clean.mat
│       │   ARAD_HS_0508_clean.mat
│       │   ...
│   └───track2
│       │   ARAD_HS_0477_RealWorld.mat
│       │   ARAD_HS_0502_RealWorld.mat
│       │   ...
│

Requirements

Python 3.6
Pytorch 1.0.0
Cuda 8.0

Train

Run train.py.
Change baseroot that contains training data.
Change save_path corresponding to track 1 or track 2.
Change other parameters.

Test

Note that the data should be first generated from different models (please run test*.py). Then compute the average of all results by running ensemble_track*_8methods.py. Finally, all the results for both tracks are saved in ./ensemble/track1 and ./ensemble/track2.

track 1 generation

Run test1.py.
It will output 8 results of 8 networks.

track 1 ensemble

Run ensemble_track1_8methods.py.
It will output 1 ensemble result of 8 generated data.

track 2 generation

Run test2.py.
It will output 8 results of 8 networks.

track 2 ensemble

Run ensemble_track2_8methods.py.
It will output 1 ensemble result of 8 generated data.

For each track, we use the "best" epoch for ensemble:

Visualize

Run train_visualize.py or validation_visualize.py or test_visualize.py.

Generated spectral images

Track 1 comparison with other methods:

* Track 2 comparison with other methods:

* Track 1 all 31 bands (400nm - 700nm) of one image:

* Track 2 all 31 bands (400nm - 700nm) of one image:

Generated infrared images

Reference

If you have any question, please do not hesitate to contact [email protected]

If you find this code useful to your research, please consider citing:

@inproceedings{zhao2020hierarchical,
  title={Hierarchical Regression Network for Spectral Reconstruction from RGB Images},
  author={Zhao, Yuzhi and Po, Lai-Man and Yan, Qiong and Liu, Wei and Lin, Tingyu},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  year={2020},
}

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