We generalize MuLUT to DNN-of-LUTs, showing its versatility in low-level vision tasks. Learn more at arXiv.
Our following work, LeRF, further extends the ability of LUT to arbitrary-scale super-resolution, making up for the regrets of MuLUT on replacing interpolation methods via achiving continuous resampling. Please learn more about LeRF at its project page.
@InProceedings{Li_2022_ECCV,
author = {Li, Jiacheng and Chen, Chang and Cheng, Zhen and Xiong, Zhiwei},
title = {MuLUT: Cooperating Multiple Look-Up Tables for Efficient Image Super-Resolution},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2022},
}
@arxiv{Li_2023_DNN_LUT,
author = {Li, Jiacheng and Chen, Chang and Cheng, Zhen and Xiong, Zhiwei},
title = {Toward {DNN} of {LUTs}: Learning Efficient Image Restoration with Multiple Look-Up Tables},
booktitle = {arxiv},
year = {2023},
}
@InProceedings{Li_2023_CVPR,
author = {Li, Jiacheng and Chen, Chang and Huang, Wei and Lang, Zhiqiang and Song, Fenglong and Yan, Youliang and Xiong, Zhiwei},
title = {Learning Steerable Function for Efficient Image Resampling},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {5866-5875}
}
We would like to thank Shiyu Deng, Bo Hu, Zeyu Xiao, and Xueyan Huang for benchmark testing, and Xihao Chen for paper revision.