MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results
<|reference_start|>MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results: 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 erformance on Few-shot RAW Image Denoising. More details of this challenge and the link to the dataset can be found at https://mipichallenge.org/MIPI2024.<|reference_end|>
@article{jin2024mipi,
    title={MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results},
    author={Xin Jin, Chunle Guo, Xiaoming Li, Zongsheng Yue, Chongyi Li, Shangchen
  Zhou, Ruicheng Feng, Yuekun Dai, Peiqing Yang, Chen Change Loy, Ruoqi Li,
  Chang Liu, Ziyi Wang, Yao Du, Jingjing Yang, Long Bao, Heng Sun, Xiangyu
  Kong, Xiaoxia Xing, Jinlong Wu, Yuanyang Xue, Hyunhee Park, Sejun Song,
  Changho Kim, Jingfan Tan, Wenhan Luo, Zikun Liu, Mingde Qiao, Junjun Jiang,
  Kui Jiang, Yao Xiao, Chuyang Sun, Jinhui Hu, Weijian Ruan, Yubo Dong, Kai
  Chen, Hyejeong Jo, Jiahao Qin, Bingjie Han, Pinle Qin, Rui Chai, Pengyuan
  Wang},
    journal={arXiv preprint arXiv:2406.07006},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07006},
    primaryClass={cs.CV}
}