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

  • 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
*Corresponding author for this work
  • Nankai University
  • Nanyang Technological University
  • Xiaomi
  • Samsung Research Institute China Beijing (SRC-B)
  • Samsung
  • Sun Yat-Sen University
  • Harbin Institute of Technology
  • Smart City Research Institute of China Electronics Technology Group Corporation
  • Xidian University
  • University of Electronic Science and Technology of China
  • Hanbat National University
  • North University of China

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024
PublisherIEEE Computer Society
Pages1153-1161
Number of pages9
ISBN (Electronic)9798350365474
DOIs
StatePublished - 2024
Event2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024 - Seattle, United States
Duration: 16 Jun 202422 Jun 2024

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024
Country/TerritoryUnited States
CitySeattle
Period16/06/2422/06/24

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