CLUT
1.0.0
兩次嘗試通過學習來壓縮3DLUT:低排放分解和哈希。較小的型號的性能更高!
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相關性較弱
學習的矩陣
3D可視化的基礎3DLUTS (左:初始身份映射。右:訓練後)
網格佔用可視化
所有可視化代碼都可以在utils/。
該倉庫的框架和Clutnet的實現是建立在Zeng等人的出色工作的基礎上:學習圖像自動3D查找表,以實時增強高性能照片。 TPAMI2020
多分辨率的懸體是根據Nvidia Tiny-Cuda-NN的快速哈希編碼實現的。
非常感謝上述工作和所有合作者的努力以及您的興趣!
衷心希望我們的工作有幫助! ? ?
@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}}