This repository serves as the official code release of the IJCAI24 paper: Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution

conda create -n hklut python=3.8
conda activate hklut
pip install -r requirements.txt

Please follow the instructions below to download the corresponding datasets and place them in the specified folder structure.

The DIV2K dataset can be download from the offical website.

data/train/DIV2K/
            /HR/*.png
            /LR/{X2, X3, X4, X8}/*.png

Please follow the instructions on BasicSR to download the SR benchmark datasets.

data/test/{Set5, Set14, B100, Urban100, Manga109}/
            /HR/*.png
            /LR_bicubic/{X2, X3, X4, X8}/*.png

We have prepared bash scripts for each stage for convenient usage.

./scripts/train.sh

./scripts/transfer.sh

./scripts/test.sh

If HKLUT has been beneficial to your research and applications, please acknowledge it by using this BibTeX citation:

@article{huang2023hundred,
  title={Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution},
  author={Huang, Binxiao and Li, Jason Chun Lok and Ran, Jie and Li, Boyu and Zhou, Jiajun and Yu, Dahai and Wong, Ngai},
  journal={arXiv preprint arXiv:2312.06101},
  year={2023}
}

Our code is built upon MuLUT and SPLUT. We sincerely appreciate their contributions to open-source.