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[CVPR 2023] B-spline Texture Coefficients Estimator for Screen Content Image Super-Resolution (Highlight)

B-spline Texture Coefficients Estimator for Screen Content Image Super-Resolution

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

[Project Page] [Paper]

Environment

Requirements

Checkpoint

Datasets

  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.

Demo

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

Train

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

Train

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

Citation

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}
}

Acknowledgements

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