CLUT
1.0.0
两次尝试通过学习来压缩3DLUT：低排放分解和哈希。较小的型号的性能更高！
☺️
相关性较弱
学习的矩阵
3D可视化的基础3DLUTS （左：初始身份映射。右：训练后） 
网格占用可视化
所有可视化代码都可以在utils/。
该仓库的框架和Clutnet的实现是建立在Zeng等人的出色工作的基础上：学习图像自动3D查找表，以实时增强高性能照片。 TPAMI2020
多分辨率的悬体是根据Nvidia Tiny-Cuda-NN的快速哈希编码实现的。
非常感谢上述工作和所有合作者的努力以及您的兴趣！
衷心希望我们的工作有帮助！ ？ ？
@inproceedings{clutnet,
author = {Zhang, Fengyi and Zeng, Hui and Zhang, Tianjun and Zhang, Lin},
title = {CLUT-Net: Learning Adaptively Compressed Representations of 3DLUTs for Lightweight Image Enhancement},
year = {2022},
isbn = {9781450392037},
url = {https://doi.org/10.1145/3503161.3547879},
doi = {10.1145/3503161.3547879},
booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
pages = {6493–6501},
numpages = {9},
}
@INPROCEEDINGS{hashlut,
author={Zhang, Fengyi and Zhang, Lin and Zhang, Tianjun and Wang, Dongqing},
booktitle={2023 IEEE International Conference on Multimedia and Expo (ICME)},
title={Adaptively Hashing 3DLUTs for Lightweight Real-time Image Enhancement},
year={2023},
volume={},
number={},
pages={2771-2776},
doi={10.1109/ICME55011.2023.00471}}
