AIM 2020 高效超分辨率挑战:方法和结果 (AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results)

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

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.

翻译：本文回顾了AIM 2020关于高效单一图像超分辨率的挑战,重点是拟议的解决方案和结果。挑战的任务是根据一套以前低和相应的高分辨率图像实例,以放大因子x4为基础,以超级解析一个具有放大因子的输入图像。目的是设计一个网络,减少运行时间、参数计数、FLOPs、激活和记忆消耗等一个或几个方面,同时至少保持MSRResNet的 PSNR。轨道有150名注册参与者,25个团队提交了最终结果。他们用高效的单一图像超分辨率测量了最新技术。

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医学人工智能AIM（Artificial Intelligence in Medicine）杂志发表了多学科领域的原创文章，涉及医学中的人工智能理论和实践，以医学为导向的人类生物学和卫生保健。医学中的人工智能可以被描述为与研究、项目和应用相关的科学学科，旨在通过基于知识或数据密集型的计算机解决方案支持基于决策的医疗任务，最终支持和改善人类护理提供者的性能。官网地址：http://dblp.uni-trier.de/db/journals/artmed/