MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results
Abstract
Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack 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). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022 .
Cite
Text
Feng et al. "MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results." European Conference on Computer Vision Workshops, 2022. doi:10.1007/978-3-031-25072-9_5Markdown
[Feng et al. "MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results." European Conference on Computer Vision Workshops, 2022.](https://mlanthology.org/eccvw/2022/feng2022eccvw-mipi/) doi:10.1007/978-3-031-25072-9_5BibTeX
@inproceedings{feng2022eccvw-mipi,
title = {{MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results}},
author = {Feng, Ruicheng and Li, Chongyi and Zhou, Shangchen and Sun, Wenxiu and Zhu, Qingpeng and Jiang, Jun and Yang, Qingyu and Loy, Chen Change and Gu, Jinwei and Zhu, Yurui and Wang, Xi and Fu, Xueyang and Hu, Xiaowei and Hu, Jinfan and Liu, Xina and Chen, Xiangyu and Dong, Chao and Zhang, Dafeng and Huang, Feiyu and Liu, Shizhuo and Wang, Xiaobing and Jin, Zhezhu and Jiang, Xuhao and Shao, Guangqi and Wang, Xiaotao and Lei, Lei and Zhang, Zhao and Zhao, Suiyi and Zheng, Huan and Gao, Yangcheng and Wei, Yanyan and Ren, Jiahuan and Huang, Tao and Fang, Zhenxuan and Huang, Mengluan and Xu, Junwei and Zhang, Yong and Yang, Yuechi and Shu, Qidi and Yang, Zhiwen and Li, Shaocong and Yao, Mingde and Xu, Ruikang and Guan, Yuanshen and Huang, Jie and Xiong, Zhiwei and Zhu, Hangyan and Liu, Ming and Liu, Shaohui and Zuo, Wangmeng and Jia, Zhuang and Song, Binbin and Song, Ziqi and Mao, Guiting and Hou, Ben and Liu, Zhimou and Ke, Yi and Ouyang, Dengpei and Han, Dekui and Zhang, Jinghao and Zhu, Qi and Zheng, Naishan and Zhao, Feng and Jin, Wu and Conde, Marcos V. and Nathan, Sabari and Timofte, Radu and Xu, Tianyi and Xu, Jun and Hrishikesh, P. S. and Puthussery, Densen and Jiji, C. V. and Jiang, Biao and Ding, Yuhan and Li, WanZhang and Feng, Xiaoyue and Chen, Sijing and Zhong, Tianheng and Lu, Jiyang and Chen, Hongming and Fan, Zhentao and Chen, Xiang},
booktitle = {European Conference on Computer Vision Workshops},
year = {2022},
pages = {60-77},
doi = {10.1007/978-3-031-25072-9_5},
url = {https://mlanthology.org/eccvw/2022/feng2022eccvw-mipi/}
}