Byeonghyun Pak*, Jaewon Lee*, Kyong Hwan Jin
Daegu Gyeongbuk Institute of Science and Technology (DGIST)
CVPR 2023, Highlight

• Python 3
• Pytorch 1.13.0
• TensorboardX
• pyyaml, numpy, tqdm, imageio

• Download a SCI1K pre-trained model: RDN-BTC

1. mkdir ../Data for putting the dataset folders.

2. cd ../Data and download the datasets (SCI1K, SCID, and SIQAD) from this repo.

3. For the additional benchmarks in Tab 6, follow Data instruction provided by this repo.

python demo.py --input [INPUT] --model [MODEL] --scale [SCALE] --output output.png --gpu [GPU]

• [INPUT] : input image's path (e.g. --input input.png).
• [MODEL] : to define the pre-trained model (e.g. --model rdn+btc-3rd.pth).
• [SCALE] : arbitrary magnification (e.g. --scale 3 or --scale 6.4).
• [GPU] : to specify the GPUS (e.g. --gpu 0).

python train.py --config configs/train/[TRAIN_CONFIG] --gpu [GPU]

• [TRAIN_CONFIG] : to define model configuration (e.g. train-rdn+btc-3rd.yaml).
• [GPU] : to specify the GPUS (e.g. --gpu 0 or --gpu 0,1).

python test.py --config configs/test/[TEST_CONFIG] --model save/[MODEL] --gpu [GPU]

• [TEST_CONFIG] : to define test configuration (e.g. test-sci1k-02.yaml for SCI1K dataset on x2).
• [MODEL] : to define the pre-trained model (e.g. rdn+btc-3rd/epoch_last.pth).
• [GPU] : to specify the GPUS (e.g. --gpu 0 or --gpu 0,1).

If you find our code helpful, please cite our paper:

@inproceedings{pak2023b,
  title     = {B-spline Texture Coefficients Estimator for Screen Content Image Super-Resolution},
  author    = {Pak, Byeonghyun and Lee, Jaewon and Jin, Kyong Hwan},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages     = {10062--10071},
  year      = {2023}
}

This project is based on the following open-source projects. We thank the authors for sharing their codes.

• LIIF
• LTE