†. MarcosV.Conde, †. EduardZamfir, R. Timofte, Daniel Motilla, Cen Liu, Zexin Zhang, Yunbo Peng, Yue Lin, Jiaming Guo, X. Zou, Yu-Yi Chen, Yi Liu, Jiangnan Hao, Youliang Yan, Yuan Zhang, Gen Li, Lei Sun, Lingshun Kong, Haoran Bai, Jin-shan Pan, Jiangxin Dong, Jinhui Tang, Mustafa Ayazoglu Bahri, Batuhan Bilecen, Mingxiu Li, Yuhang Zhang, Xianjun Fan, Yan Sheng, Long Sun, Zibin Liu, Weiran Gou, Sha Li, Ziyao Yi, Yan Xiang, Dehui Kong, Ke Xu, G. Gankhuyag, Kuk-jin Yoon, Jin Zhang, G. Yu, Feng Zhang, Hongbin Wang, Zhou Zhou, Jiahao Chao, Hong-Xin Gao, Jiali Gong, Zhengfeng Yang, Zhenbing Zeng, Cheng-An Chen, Zichao Guo, Anjin Park, Yu Qi, Hongyuan Jia, Xuan Yu, K. Yin, Dongyang Zuo, Zhang Ting, Zhengxue Fu, Cheng Shiai, Dajiang Zhu, Hong Zhou, Weichen Yu, Jiahua Dong, Yajun Zou, Zhuoyuan Wu, B. Han, Xiaolin Zhang, He Zhang, X. Yin, Benke Shao, Shaolong Zheng, Daheng Yin, Baijun Chen, Mengyang Liu, Marian-Sergiu Nistor, Yi-Chung Chen, Zhi-Kai Huang, Yuan Chiang, Wei-Ting Chen, Hao Yang, Hua-En Chang, I-Hsiang
{"title":"实时4K图像超分辨率的高效深度模型。2023年全年基准和报告","authors":"†. MarcosV.Conde, †. EduardZamfir, R. Timofte, Daniel Motilla, Cen Liu, Zexin Zhang, Yunbo Peng, Yue Lin, Jiaming Guo, X. Zou, Yu-Yi Chen, Yi Liu, Jiangnan Hao, Youliang Yan, Yuan Zhang, Gen Li, Lei Sun, Lingshun Kong, Haoran Bai, Jin-shan Pan, Jiangxin Dong, Jinhui Tang, Mustafa Ayazoglu Bahri, Batuhan Bilecen, Mingxiu Li, Yuhang Zhang, Xianjun Fan, Yan Sheng, Long Sun, Zibin Liu, Weiran Gou, Sha Li, Ziyao Yi, Yan Xiang, Dehui Kong, Ke Xu, G. Gankhuyag, Kuk-jin Yoon, Jin Zhang, G. Yu, Feng Zhang, Hongbin Wang, Zhou Zhou, Jiahao Chao, Hong-Xin Gao, Jiali Gong, Zhengfeng Yang, Zhenbing Zeng, Cheng-An Chen, Zichao Guo, Anjin Park, Yu Qi, Hongyuan Jia, Xuan Yu, K. Yin, Dongyang Zuo, Zhang Ting, Zhengxue Fu, Cheng Shiai, Dajiang Zhu, Hong Zhou, Weichen Yu, Jiahua Dong, Yajun Zou, Zhuoyuan Wu, B. Han, Xiaolin Zhang, He Zhang, X. Yin, Benke Shao, Shaolong Zheng, Daheng Yin, Baijun Chen, Mengyang Liu, Marian-Sergiu Nistor, Yi-Chung Chen, Zhi-Kai Huang, Yuan Chiang, Wei-Ting Chen, Hao Yang, Hua-En Chang, I-Hsiang","doi":"10.1109/CVPRW59228.2023.00154","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":355438,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report\",\"authors\":\"†. MarcosV.Conde, †. EduardZamfir, R. Timofte, Daniel Motilla, Cen Liu, Zexin Zhang, Yunbo Peng, Yue Lin, Jiaming Guo, X. Zou, Yu-Yi Chen, Yi Liu, Jiangnan Hao, Youliang Yan, Yuan Zhang, Gen Li, Lei Sun, Lingshun Kong, Haoran Bai, Jin-shan Pan, Jiangxin Dong, Jinhui Tang, Mustafa Ayazoglu Bahri, Batuhan Bilecen, Mingxiu Li, Yuhang Zhang, Xianjun Fan, Yan Sheng, Long Sun, Zibin Liu, Weiran Gou, Sha Li, Ziyao Yi, Yan Xiang, Dehui Kong, Ke Xu, G. Gankhuyag, Kuk-jin Yoon, Jin Zhang, G. Yu, Feng Zhang, Hongbin Wang, Zhou Zhou, Jiahao Chao, Hong-Xin Gao, Jiali Gong, Zhengfeng Yang, Zhenbing Zeng, Cheng-An Chen, Zichao Guo, Anjin Park, Yu Qi, Hongyuan Jia, Xuan Yu, K. Yin, Dongyang Zuo, Zhang Ting, Zhengxue Fu, Cheng Shiai, Dajiang Zhu, Hong Zhou, Weichen Yu, Jiahua Dong, Yajun Zou, Zhuoyuan Wu, B. Han, Xiaolin Zhang, He Zhang, X. Yin, Benke Shao, Shaolong Zheng, Daheng Yin, Baijun Chen, Mengyang Liu, Marian-Sergiu Nistor, Yi-Chung Chen, Zhi-Kai Huang, Yuan Chiang, Wei-Ting Chen, Hao Yang, Hua-En Chang, I-Hsiang\",\"doi\":\"10.1109/CVPRW59228.2023.00154\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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. 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Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report
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.
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