目标2020挑战有效的超分辨率：方法和结果

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目标2020挑战有效的超分辨率：方法和结果

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

论文作者

Zhang, Kai, Danelljan, Martin, Li, Yawei, Timofte, Radu, Liu, Jie, Tang, Jie, Wu, Gangshan, Zhu, Yu, He, Xiangyu, Xu, Wenjie, Li, Chenghua, Leng, Cong, Cheng, Jian, Wu, Guangyang, Wang, Wenyi, Liu, Xiaohong, Zhao, Hengyuan, Kong, Xiangtao, He, Jingwen, Qiao, Yu, Dong, Chao, Luo, Xiaotong, Chen, Liang, Zhang, Jiangtao, Suin, Maitreya, Purohit, Kuldeep, Rajagopalan, A. N., Li, Xiaochuan, Lang, Zhiqiang, Nie, Jiangtao, Wei, Wei, Zhang, Lei, Muqeet, Abdul, Hwang, Jiwon, Yang, Subin, Kang, JungHeum, Bae, Sung-Ho, Kim, Yongwoo, Chen, Liang, Zhang, Jiangtao, Luo, Xiaotong, Qu, Yanyun, Jeon, Geun-Woo, Choi, Jun-Ho, Kim, Jun-Hyuk, Lee, Jong-Seok, Marty, Steven, Marty, Eric, Xiong, Dongliang, Chen, Siang, Zha, Lin, Jiang, Jiande, Gao, Xinbo, Lu, Wen, Wang, Haicheng, Bhaskara, Vineeth, Levinshtein, Alex, Tsogkas, Stavros, Jepson, Allan, Kong, Xiangzhen, Zhao, Tongtong, Zhao, Shanshan, S, Hrishikesh P, Puthussery, Densen, C V, Jiji, Nan, Nan, Liu, Shuai, Cai, Jie, Meng, Zibo, Ding, Jiaming, Ho, Chiu Man, Wang, Xuehui, Yan, Qiong, Zhao, Yuzhi, Chen, Long, Zhang, Jiangtao, Luo, Xiaotong, Chen, Liang, Qu, Yanyun, Sun, Long, Wang, Wenhao, Liu, Zhenbing, Lan, Rushi, Umer, Rao Muhammad, Micheloni, Christian

论文摘要

本文回顾了针对有效的单图超分辨率的AIM 2020挑战，重点是拟议的解决方案和结果。挑战任务是基于一组先前的低和相应高分辨率图像的先前示例，具有放大因子X4的超级溶解输入图像。目的是设计一个网络，该网络减少一个或几个方面，例如运行时，参数计数，拖放，激活和内存消耗，而至少维护MSRRESNET的PSNR。该赛道有150名注册参与者，25个团队提交了最终结果。他们在有效的单图像超分辨率中评估了最新的。

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.

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