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[pdf]
[arXiv]
[bibtex]@InProceedings{Jin_2024_CVPR, author = {Jin, Xin and Guo, Chunle and Li, Xiaoming and Yue, Zongsheng and Li, Chongyi and Zhou, Shangchen and Feng, Ruicheng and Dai, Yuekun and Yang, Peiqing and Loy, Chen Change and Li, Ruoqi and Liu, Chang and Wang, Ziyi and Du, Yao and Yang, Jingjing and Bao, Long and Sun, Heng and Kong, Xiangyu and Xing, Xiaoxia and Wu, Jinlong and Xue, Yuanyang and Park, Hyunhee and Song, Sejun and Kim, Changho and Tan, Jingfan and Luo, Wenhan and Liu, Zikun and Qiao, Mingde and Jiang, Junjun and Jiang, Kui and Xiao, Yao and Sun, Chuyang and Hu, Jinhui and Ruan, Weijian and Dong, Yubo and Chen, Kai and Jo, Hyejeong and Qin, Jiahao and Han, Bingjie and Qin, Pinle and Chai, Rui and Wang, Pengyuan}, title = {MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {1153-1161} }
MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results
Abstract
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and CVPR 2023 we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper we summarize and review the Few-shot RAW Image Denoising track on MIPI 2024. In total 165 participants were successfully registered and 7 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Few-shot RAW Image Denoising. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2024.
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