关于高效超级决议:方法和成果的挑战 (NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results)

Yawei Li,Kai Zhang,Radu Timofte,Luc Van Gool,Fangyuan Kong,Mingxi Li,Songwei Liu,Zongcai Du,Ding Liu,Chenhui Zhou,Jingyi Chen,Qingrui Han,Zheyuan Li,Yingqi Liu,Xiangyu Chen,Haoming Cai,Yu Qiao,Chao Dong,Long Sun,Jinshan Pan,Yi Zhu,Zhikai Zong,Xiaoxiao Liu,Zheng Hui,Tao Yang,Peiran Ren,Xuansong Xie,Xian-Sheng Hua,Yanbo Wang,Xiaozhong Ji,Chuming Lin,Donghao Luo,Ying Tai,Chengjie Wang,Zhizhong Zhang,Yuan Xie,Shen Cheng,Ziwei Luo,Lei Yu,Zhihong Wen,Qi Wu1,Youwei Li,Haoqiang Fan,Jian Sun,Shuaicheng Liu,Yuanfei Huang,Meiguang Jin,Hua Huang,Jing Liu,Xinjian Zhang,Yan Wang,Lingshun Long,Gen Li,Yuanfan Zhang,Zuowei Cao,Lei Sun,Panaetov Alexander,Yucong Wang,Minjie Cai,Li Wang,Lu Tian,Zheyuan Wang,Hongbing Ma,Jie Liu,Chao Chen,Yidong Cai,Jie Tang,Gangshan Wu,Weiran Wang,Shirui Huang,Honglei Lu,Huan Liu,Keyan Wang,Jun Chen,Shi Chen,Yuchun Miao,Zimo Huang,Lefei Zhang,Mustafa Ayazoğlu,Wei Xiong,Chengyi Xiong,Fei Wang,Hao Li,Ruimian Wen,Zhijing Yang,Wenbin Zou,Weixin Zheng,Tian Ye,Yuncheng Zhang,Xiangzhen Kong,Aditya Arora,Syed Waqas Zamir,Salman Khan,Munawar Hayat,Fahad Shahbaz Khan,Dandan Gaoand Dengwen Zhouand Qian Ning,Jingzhu Tang,Han Huang,Yufei Wang,Zhangheng Peng,Haobo Li,Wenxue Guan,Shenghua Gong,Xin Li,Jun Liu,Wanjun Wang,Dengwen Zhou,Kun Zeng,Hanjiang Lin,Xinyu Chen,Jinsheng Fang

from arxiv, Validation code of the baseline model is available at https://github.com/ofsoundof/IMDN. Validation of all submitted models is available at https://github.com/ofsoundof/NTIRE2022_ESR

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of $\times$4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.

翻译：本文回顾了2022年关于高效单一图像超级分辨率的NTIRE 2022挑战,重点是拟议的解决方案和结果。挑战的任务是根据低分辨率和相应高分辨率图像组合,以4美元作为放大因数,超级解决投入因数,其中4美元以低分辨率和相应高分辨率图像组合为基础。目的是设计一个单一图像超级分辨率网络,根据包括运行时间、参数、FLOP、启动和记忆消耗等数等数度衡量效率的提高,同时至少维持DIV2K验证装置上29.00dB的PSNRR。IMDN被设定为效率衡量的基线。挑战有3个轨道,包括主轨(运行时间)、分轨1(模型复杂程度)和亚轨2(总体性业绩)。在主要轨道上,对提交材料的实际运行时间进行了评估,这些团队的级别直接取决于验证装置和测试设置的平均运行时间的绝对值。在次轨迹中,审议了参数和FLOP的数值的数值和FLOP值数值的数值。在最后排名中,两个指标组的排名中,在最后的排名中,在排序中,在最后排序中,在排序中,在排序前的排名中,在排序中,在排序中,所有排序中,在排序中,在排序中,在排序中,在排序中,在排序中,在排序中,在排序中,在排序中,在排序中,在排序中,在排序中,在排序中,在排序中,在排序中,在排序前,在排序前,在排序前,在排序前,在排序前,在排序中,在排序中,在排序中,在排序中,在排序中,在排序中,在排序前,在排序中,在排序中,在排序中,在排序前,在排序中,在排序中,在排序中,在排序中,在排序前,在排序前,在排序前,在排序前,在排序前,在排序前,在排序前,所有。