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The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report

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Main Authors:	Ren, Bin, Li, Yawei, Mehta, Nancy, Timofte, Radu, Yu, Hongyuan, Wan, Cheng, Hong, Yuxin, Han, Bingnan, Wu, Zhuoyuan, Zou, Yajun, Liu, Yuqing, Li, Jizhe, He, Keji, Fan, Chao, Zhang, Heng, Zhang, Xiaolin, Yin, Xuanwu, Zuo, Kunlong, Liao, Bohao, Xia, Peizhe, Peng, Long, Du, Zhibo, Di, Xin, Li, Wangkai, Wang, Yang, Zhai, Wei, Pei, Renjing, Guo, Jiaming, Xu, Songcen, Cao, Yang, Zha, Zhengjun, Wang, Yan, Liu, Yi, Wang, Qing, Zhang, Gang, Zhang, Liou, Zhao, Shijie, Sun, Long, Pan, Jinshan, Dong, Jiangxin, Tang, Jinhui, Liu, Xin, Yan, Min, Wang, Qian, Zhou, Menghan, Yan, Yiqiang, Liu, Yixuan, Chan, Wensong, Tang, Dehua, Zhou, Dong, Wang, Li, Tian, Lu, Emad, Barsoum, Jia, Bohan, Qiao, Junbo, Zhou, Yunshuai, Zhang, Yun, Li, Wei, Lin, Shaohui, Zhou, Shenglong, Chen, Binbin, Liao, Jincheng, Zhao, Suiyi, Zhang, Zhao, Wang, Bo, Luo, Yan, Wei, Yanyan, Li, Feng, Wang, Mingshen, Guan, Jinhan, Hu, Dehua, Yu, Jiawei, Xu, Qisheng, Sun, Tao, Lan, Long, Xu, Kele, Lin, Xin, Yue, Jingtong, Yang, Lehan, Du, Shiyi, Qi, Lu, Ren, Chao, Han, Zeyu, Wang, Yuhan, Chen, Chaolin, Li, Haobo, Zheng, Mingjun, Yang, Zhongbao, Song, Lianhong, Yan, Xingzhuo, Fu, Minghan, Zhang, Jingyi, Li, Baiang, Zhu, Qi, Xu, Xiaogang, Guo, Dan, Guo, Chunle, Chen, Jiadi, Long, Huanhuan, Duanmu, Chunjiang, Lei, Xiaoyan, Liu, Jie, Jia, Weilin, Cao, Weifeng, Zhang, Wenlong, Mao, Yanyu, Guo, Ruilong, Zhang, Nihao, Pandey, Manoj, Chernozhukov, Maksym, Le, Giang, Cheng, Shuli, Wang, Hongyuan, Wei, Ziyan, Tang, Qingting, Wang, Liejun, Li, Yongming, Guo, Yanhui, Xu, Hao, Khatami-Rizi, Akram, Mahmoudi-Aznaveh, Ahmad, Hsu, Chih-Chung, Lee, Chia-Ming, Chou, Yi-Shiuan, Joshi, Amogh, Akalwadi, Nikhil, Malagi, Sampada, Yashaswini, Palani, Desai, Chaitra, Tabib, Ramesh Ashok, Patil, Ujwala, Mudenagudi, Uma
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Image and Video Processing
Online Access:	Acceder al recurso
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author	Ren, Bin Li, Yawei Mehta, Nancy Timofte, Radu Yu, Hongyuan Wan, Cheng Hong, Yuxin Han, Bingnan Wu, Zhuoyuan Zou, Yajun Liu, Yuqing Li, Jizhe He, Keji Fan, Chao Zhang, Heng Zhang, Xiaolin Yin, Xuanwu Zuo, Kunlong Liao, Bohao Xia, Peizhe Peng, Long Du, Zhibo Di, Xin Li, Wangkai Wang, Yang Zhai, Wei Pei, Renjing Guo, Jiaming Xu, Songcen Cao, Yang Zha, Zhengjun Wang, Yan Liu, Yi Wang, Qing Zhang, Gang Zhang, Liou Zhao, Shijie Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Liu, Xin Yan, Min Wang, Qian Zhou, Menghan Yan, Yiqiang Liu, Yixuan Chan, Wensong Tang, Dehua Zhou, Dong Wang, Li Tian, Lu Emad, Barsoum Jia, Bohan Qiao, Junbo Zhou, Yunshuai Zhang, Yun Li, Wei Lin, Shaohui Zhou, Shenglong Chen, Binbin Liao, Jincheng Zhao, Suiyi Zhang, Zhao Wang, Bo Luo, Yan Wei, Yanyan Li, Feng Wang, Mingshen Li, Yawei Guan, Jinhan Hu, Dehua Yu, Jiawei Xu, Qisheng Sun, Tao Lan, Long Xu, Kele Lin, Xin Yue, Jingtong Yang, Lehan Du, Shiyi Qi, Lu Ren, Chao Han, Zeyu Wang, Yuhan Chen, Chaolin Li, Haobo Zheng, Mingjun Yang, Zhongbao Song, Lianhong Yan, Xingzhuo Fu, Minghan Zhang, Jingyi Li, Baiang Zhu, Qi Xu, Xiaogang Guo, Dan Guo, Chunle Chen, Jiadi Long, Huanhuan Duanmu, Chunjiang Lei, Xiaoyan Liu, Jie Jia, Weilin Cao, Weifeng Zhang, Wenlong Mao, Yanyu Guo, Ruilong Zhang, Nihao Wang, Qian Pandey, Manoj Chernozhukov, Maksym Le, Giang Cheng, Shuli Wang, Hongyuan Wei, Ziyan Tang, Qingting Wang, Liejun Li, Yongming Guo, Yanhui Xu, Hao Khatami-Rizi, Akram Mahmoudi-Aznaveh, Ahmad Hsu, Chih-Chung Lee, Chia-Ming Chou, Yi-Shiuan Joshi, Amogh Akalwadi, Nikhil Malagi, Sampada Yashaswini, Palani Desai, Chaitra Tabib, Ramesh Ashok Patil, Ujwala Mudenagudi, Uma
author_facet	Ren, Bin Li, Yawei Mehta, Nancy Timofte, Radu Yu, Hongyuan Wan, Cheng Hong, Yuxin Han, Bingnan Wu, Zhuoyuan Zou, Yajun Liu, Yuqing Li, Jizhe He, Keji Fan, Chao Zhang, Heng Zhang, Xiaolin Yin, Xuanwu Zuo, Kunlong Liao, Bohao Xia, Peizhe Peng, Long Du, Zhibo Di, Xin Li, Wangkai Wang, Yang Zhai, Wei Pei, Renjing Guo, Jiaming Xu, Songcen Cao, Yang Zha, Zhengjun Wang, Yan Liu, Yi Wang, Qing Zhang, Gang Zhang, Liou Zhao, Shijie Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Liu, Xin Yan, Min Wang, Qian Zhou, Menghan Yan, Yiqiang Liu, Yixuan Chan, Wensong Tang, Dehua Zhou, Dong Wang, Li Tian, Lu Emad, Barsoum Jia, Bohan Qiao, Junbo Zhou, Yunshuai Zhang, Yun Li, Wei Lin, Shaohui Zhou, Shenglong Chen, Binbin Liao, Jincheng Zhao, Suiyi Zhang, Zhao Wang, Bo Luo, Yan Wei, Yanyan Li, Feng Wang, Mingshen Li, Yawei Guan, Jinhan Hu, Dehua Yu, Jiawei Xu, Qisheng Sun, Tao Lan, Long Xu, Kele Lin, Xin Yue, Jingtong Yang, Lehan Du, Shiyi Qi, Lu Ren, Chao Han, Zeyu Wang, Yuhan Chen, Chaolin Li, Haobo Zheng, Mingjun Yang, Zhongbao Song, Lianhong Yan, Xingzhuo Fu, Minghan Zhang, Jingyi Li, Baiang Zhu, Qi Xu, Xiaogang Guo, Dan Guo, Chunle Chen, Jiadi Long, Huanhuan Duanmu, Chunjiang Lei, Xiaoyan Liu, Jie Jia, Weilin Cao, Weifeng Zhang, Wenlong Mao, Yanyu Guo, Ruilong Zhang, Nihao Wang, Qian Pandey, Manoj Chernozhukov, Maksym Le, Giang Cheng, Shuli Wang, Hongyuan Wei, Ziyan Tang, Qingting Wang, Liejun Li, Yongming Guo, Yanhui Xu, Hao Khatami-Rizi, Akram Mahmoudi-Aznaveh, Ahmad Hsu, Chih-Chung Lee, Chia-Ming Chou, Yi-Shiuan Joshi, Amogh Akalwadi, Nikhil Malagi, Sampada Yashaswini, Palani Desai, Chaitra Tabib, Ramesh Ashok Patil, Ujwala Mudenagudi, Uma
contents	This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this challenge is to super-resolve an input image with a magnification factor of x4 based on pairs of low and corresponding high-resolution images. The primary objective is to develop networks that optimize various aspects such as runtime, parameters, and FLOPs, while still maintaining a peak signal-to-noise ratio (PSNR) of approximately 26.90 dB on the DIV2K_LSDIR_valid dataset and 26.99 dB on the DIV2K_LSDIR_test dataset. In addition, this challenge has 4 tracks including the main track (overall performance), sub-track 1 (runtime), sub-track 2 (FLOPs), and sub-track 3 (parameters). In the main track, all three metrics (ie runtime, FLOPs, and parameter count) were considered. The ranking of the main track is calculated based on a weighted sum-up of the scores of all other sub-tracks. In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking. In sub-track 2, the number of FLOPs was considered. The score calculated based on the corresponding FLOPs was used to determine the ranking. In sub-track 3, the number of parameters was considered. The score calculated based on the corresponding parameters was used to determine the ranking. RLFN is set as the baseline for efficiency measurement. The challenge had 262 registered participants, and 34 teams made valid submissions. They gauge the state-of-the-art in efficient single-image super-resolution. To facilitate the reproducibility of the challenge and enable other researchers to build upon these findings, the code and the pre-trained model of validated solutions are made publicly available at https://github.com/Amazingren/NTIRE2024_ESR/.
format	Preprint
id	arxiv_https___arxiv_org_abs_2404_10343
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report Ren, Bin Li, Yawei Mehta, Nancy Timofte, Radu Yu, Hongyuan Wan, Cheng Hong, Yuxin Han, Bingnan Wu, Zhuoyuan Zou, Yajun Liu, Yuqing Li, Jizhe He, Keji Fan, Chao Zhang, Heng Zhang, Xiaolin Yin, Xuanwu Zuo, Kunlong Liao, Bohao Xia, Peizhe Peng, Long Du, Zhibo Di, Xin Li, Wangkai Wang, Yang Zhai, Wei Pei, Renjing Guo, Jiaming Xu, Songcen Cao, Yang Zha, Zhengjun Wang, Yan Liu, Yi Wang, Qing Zhang, Gang Zhang, Liou Zhao, Shijie Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Liu, Xin Yan, Min Wang, Qian Zhou, Menghan Yan, Yiqiang Liu, Yixuan Chan, Wensong Tang, Dehua Zhou, Dong Wang, Li Tian, Lu Emad, Barsoum Jia, Bohan Qiao, Junbo Zhou, Yunshuai Zhang, Yun Li, Wei Lin, Shaohui Zhou, Shenglong Chen, Binbin Liao, Jincheng Zhao, Suiyi Zhang, Zhao Wang, Bo Luo, Yan Wei, Yanyan Li, Feng Wang, Mingshen Li, Yawei Guan, Jinhan Hu, Dehua Yu, Jiawei Xu, Qisheng Sun, Tao Lan, Long Xu, Kele Lin, Xin Yue, Jingtong Yang, Lehan Du, Shiyi Qi, Lu Ren, Chao Han, Zeyu Wang, Yuhan Chen, Chaolin Li, Haobo Zheng, Mingjun Yang, Zhongbao Song, Lianhong Yan, Xingzhuo Fu, Minghan Zhang, Jingyi Li, Baiang Zhu, Qi Xu, Xiaogang Guo, Dan Guo, Chunle Chen, Jiadi Long, Huanhuan Duanmu, Chunjiang Lei, Xiaoyan Liu, Jie Jia, Weilin Cao, Weifeng Zhang, Wenlong Mao, Yanyu Guo, Ruilong Zhang, Nihao Wang, Qian Pandey, Manoj Chernozhukov, Maksym Le, Giang Cheng, Shuli Wang, Hongyuan Wei, Ziyan Tang, Qingting Wang, Liejun Li, Yongming Guo, Yanhui Xu, Hao Khatami-Rizi, Akram Mahmoudi-Aznaveh, Ahmad Hsu, Chih-Chung Lee, Chia-Ming Chou, Yi-Shiuan Joshi, Amogh Akalwadi, Nikhil Malagi, Sampada Yashaswini, Palani Desai, Chaitra Tabib, Ramesh Ashok Patil, Ujwala Mudenagudi, Uma Computer Vision and Pattern Recognition Image and Video Processing This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this challenge is to super-resolve an input image with a magnification factor of x4 based on pairs of low and corresponding high-resolution images. The primary objective is to develop networks that optimize various aspects such as runtime, parameters, and FLOPs, while still maintaining a peak signal-to-noise ratio (PSNR) of approximately 26.90 dB on the DIV2K_LSDIR_valid dataset and 26.99 dB on the DIV2K_LSDIR_test dataset. In addition, this challenge has 4 tracks including the main track (overall performance), sub-track 1 (runtime), sub-track 2 (FLOPs), and sub-track 3 (parameters). In the main track, all three metrics (ie runtime, FLOPs, and parameter count) were considered. The ranking of the main track is calculated based on a weighted sum-up of the scores of all other sub-tracks. In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking. In sub-track 2, the number of FLOPs was considered. The score calculated based on the corresponding FLOPs was used to determine the ranking. In sub-track 3, the number of parameters was considered. The score calculated based on the corresponding parameters was used to determine the ranking. RLFN is set as the baseline for efficiency measurement. The challenge had 262 registered participants, and 34 teams made valid submissions. They gauge the state-of-the-art in efficient single-image super-resolution. To facilitate the reproducibility of the challenge and enable other researchers to build upon these findings, the code and the pre-trained model of validated solutions are made publicly available at https://github.com/Amazingren/NTIRE2024_ESR/.
title	The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
topic	Computer Vision and Pattern Recognition Image and Video Processing
url	https://arxiv.org/abs/2404.10343