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MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results

11 June 2024
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
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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 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.

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