학술논문
NTIRE 2022 Burst Super-Resolution Challenge
Document Type
Conference
Author
Bhat, Goutam; Danelljan, Martin; Timofte, Radu; Cao, Yizhen; Cao, Yuntian; Chen, Meiya; Chen, Xihao; Cheng, Shen; Dudhane, Akshay; Fan, Haoqiang; Gang, Ruipeng; Gao, Jian; Gu, Yan; Huang, Jie; Huang, Liufeng; Jo, Youngsu; Kang, Sukju; Khan, Salman; Shahbaz Khan, Fahad; Kondo, Yuki; Li, Chenghua; Li, Fangya; Li, Jinjing; Li, Youwei; Li, Zechao; Liu, Chenming; Liu, Shuaicheng; Liu, Zikun; Liu, Zhuoming; Luo, Ziwei; Luo, Zhengxiong; Mehta, Nancy; Murala, Subrahmanyam; Nam, Yoonchan; Nakatani, Chihiro; Ostyakov, Pavel; Pan, Jinshan; Song, Ge; Sun, Jian; Sun, Long; Tang, Jinhui; Ukita, Norimichi; Wen, Zhihong; Wu, Qi; Wu, Xiaohe; Xiao, Zeyu; Xiong, Zhiwei; Xu, Rongjian; Xu, Ruikang; Yan, Youliang; Yang, Jialin; Yang, Wentao; Yang, Zhongbao; Yasue, Fuma; Yao, Mingde; Yu, Lei; Zhang, Cong; Waqas Zamir, Syed; Zhang, Jianxing; Zhang, Shuohao; Zhang, Zhilu; Zheng, Qian; Zhou, Gaofeng; Zhussip, Magauiya; Zou, Xueyi; Zuo, Wangmeng
Source
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) CVPRW Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 IEEE/CVF Conference on. :1040-1060 Jun, 2022
Subject
Language
ISSN
2160-7516
Abstract
Burst super-resolution has received increased attention in recent years due to its applications in mobile photography. By merging information from multiple shifted images of a scene, burst super-resolution aims to recover details which otherwise cannot be obtained using a simple input image. This paper reviews the NTIRE 2022 challenge on burst super-resolution. In the challenge, the participants were tasked with generating a clean RGB image with 4× higher resolution, given a RAW noisy burst as input. That is, the methods need to perform joint denoising, demosaicking, and super-resolution. The challenge consisted of 2 tracks. Track 1 employed synthetic data, where pixel-accurate high-resolution ground truths are available. Track 2 on the other hand used real-world bursts captured from a handheld camera, along with approximately aligned reference images captured using a DSLR. 14 teams participated in the final testing phase. The top performing methods establish a new state-of-the-art on the burst super-resolution task.