MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results
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| Formato: | Preprint |
| Publicado: |
2024
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| author | Jin, Xin Guo, Chunle Li, Xiaoming Yue, Zongsheng Li, Chongyi Zhou, Shangchen Feng, Ruicheng Dai, Yuekun Yang, Peiqing Loy, Chen Change Li, Ruoqi Liu, Chang Wang, Ziyi Du, Yao Yang, Jingjing Bao, Long Sun, Heng Kong, Xiangyu Xing, Xiaoxia Wu, Jinlong Xue, Yuanyang Park, Hyunhee Song, Sejun Kim, Changho Tan, Jingfan Luo, Wenhan Liu, Zikun Qiao, Mingde Jiang, Junjun Jiang, Kui Xiao, Yao Sun, Chuyang Hu, Jinhui Ruan, Weijian Dong, Yubo Chen, Kai Jo, Hyejeong Qin, Jiahao Han, Bingjie Qin, Pinle Chai, Rui Wang, Pengyuan |
| author_facet | Jin, Xin Guo, Chunle Li, Xiaoming Yue, Zongsheng Li, Chongyi Zhou, Shangchen Feng, Ruicheng Dai, Yuekun Yang, Peiqing Loy, Chen Change Li, Ruoqi Liu, Chang Wang, Ziyi Du, Yao Yang, Jingjing Bao, Long Sun, Heng Kong, Xiangyu Xing, Xiaoxia Wu, Jinlong Xue, Yuanyang Park, Hyunhee Song, Sejun Kim, Changho Tan, Jingfan Luo, Wenhan Liu, Zikun Qiao, Mingde Jiang, Junjun Jiang, Kui Xiao, Yao Sun, Chuyang Hu, Jinhui Ruan, Weijian Dong, Yubo Chen, Kai Jo, Hyejeong Qin, Jiahao Han, Bingjie Qin, Pinle Chai, Rui Wang, Pengyuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07006 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results Jin, Xin Guo, Chunle Li, Xiaoming Yue, Zongsheng Li, Chongyi Zhou, Shangchen Feng, Ruicheng Dai, Yuekun Yang, Peiqing Loy, Chen Change Li, Ruoqi Liu, Chang Wang, Ziyi Du, Yao Yang, Jingjing Bao, Long Sun, Heng Kong, Xiangyu Xing, Xiaoxia Wu, Jinlong Xue, Yuanyang Park, Hyunhee Song, Sejun Kim, Changho Tan, Jingfan Luo, Wenhan Liu, Zikun Qiao, Mingde Jiang, Junjun Jiang, Kui Xiao, Yao Sun, Chuyang Hu, Jinhui Ruan, Weijian Dong, Yubo Chen, Kai Jo, Hyejeong Qin, Jiahao Han, Bingjie Qin, Pinle Chai, Rui Wang, Pengyuan Computer Vision and Pattern Recognition 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. |
| title | MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.07006 |