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Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report

  • Marcos V. Conde*
  • , Eduard Zamfir
  • , Radu Timofte
  • , Daniel Motilla
  • , Cen Liu
  • , Zexin Zhang
  • , Yunbo Peng
  • , Yue Lin
  • , Jiaming Guo
  • , Xueyi Zou
  • , Yuyi Chen
  • , Yi Liu
  • , Jia Hao
  • , Youliang Yan
  • , Yuanfan Zhang
  • , Gen Li
  • , Lei Sun
  • , Lingshun Kong
  • , Haoran Bai
  • , Jinshan Pan
  • Jiangxin Dong, Jinhui Tang, Mustafa Ayazoglu, Bahri Batuhan Bilecen, Mingxi Li, Yuhang Zhang, Xianjun Fan, Yankai Sheng, Long Sun, Zibin Liu, Weiran Gou, Shaoqing Li, Ziyao Yi, Yan Xiang, Dehui Kong, Ke Xu, Ganzorig Gankhuyag, Kihwan Yoon, Jin Zhang, Gaocheng Yu, Feng Zhang, Hongbin Wang, Zhou Zhou, Jiahao Chao, Hongfan Gao, Jiali Gong, Zhengfeng Yang, Zhenbing Zeng, Chengpeng Chen, Zichao Guo, Anjin Park, Yuqing Liu, Qi Jia, Hongyuan Yu, Xuanwu Yin, Kunlong Zuo, Dongyang Zhang, Ting Fu, Zhengxue Cheng, Shiai Zhu, Dajiang Zhou, Weichen Yu, Lin Ge, Jiahua Dong, Yajun Zou, Zhuoyuan Wu, Binnan Han, Xiaolin Zhang, Heng Zhang, Ben Shao, Shaolong Zheng, Daheng Yin, Baijun Chen, Mengyang Liu, Marian Sergiu Nistor, Yi Chung Chen, Zhi Kai Huang, Yuan Chun Chiang, Wei Ting Chen, Hao Hsiang Yang, Hua En Chang, I. Hsiang Chen, Chia Hsuan Hsieh, Sy Yen Kuo, Tu Vo, Qingsen Yan, Yun Zhu, Jinqiu Su, Yanning Zhang, Cheng Zhang, Jiaying Luo, Youngsun Cho, Nakyung Lee
*此作品的通信作者
  • University of Würzburg
  • Sony Group Corporation
  • NetEase Games AI Lab
  • Huawei Technologies Co., Ltd.
  • Tencent
  • Nanjing University of Science and Technology
  • Attrsense
  • Sanechips Co Ltd
  • Korea Electronics Technology Institute
  • Ant Group
  • East China Normal University
  • Shopee
  • Korea Photonic Technology Institute
  • Dalian University of Technology
  • Mango TV (MGTV)
  • Xiaomi
  • Ltd.
  • Southeast University, Nanjing
  • Alexandru Ioan Cuza University of Iaşi
  • National Taiwan University
  • ServiceNow
  • KC Machine Learning Lab
  • Northwestern Polytechnical University Xian
  • CJ OliveNetworks AI Research

研究成果: 圖書/報告稿件的類型 › 會議稿件 › 同行評審

44 引文 斯高帕斯(Scopus)

摘要

This paper introduces a novel benchmark for efficient up-scaling as part of the NTIRE 2023 Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images from 720p and 1080p resolution to native 4K (×2 and ×3 factors) in real-time on commercial GPUs. For this, we use a new test set containing diverse 4K images ranging from digital art to gaming and photography. We assessed the methods devised for 4K SR by measuring their runtime, parameters, and FLOPs, while ensuring a minimum PSNR fidelity over Bicubic interpolation. Out of the 170 participants, 25 teams contributed to this report, making it the most comprehensive benchmark to date and showcasing the latest advancements in real-time SR.

原文英語
主出版物標題Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
發行者IEEE Computer Society
頁面1495-1521
頁數27
ISBN(電子)9798350302493
DOIs
出版狀態已出版 - 2023
對外發佈是
事件2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023 - Vancouver, 加拿大
持續時間: 18 06 2023 → 22 06 2023

出版系列

名字IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
卷2023-June
ISSN(列印)2160-7508
ISSN(電子)2160-7516

Conference

Conference2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
國家/地區加拿大
城市Vancouver
期間18/06/23 → 22/06/23

文獻附註

Publisher Copyright:
© 2023 IEEE.

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