The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview
/ Authors
Kai Liu, Hao Yue, Ze-Yu Lin, Zheng Chen, Jingkai Wang, Jue Gong, Jiatong Li, Xianglong Yan, Libo Zhu, Jianze Li
and 58 more authors
Ziqing Zhang, Zihan Zhou, Xiaoyang Liu, R. Timofte, Yulun Zhang, Junye Chen, Zhe Yan, Yucong Hong, Ruize Han, Song Wang, Li Pang, Hengwei Zhao, Xinqiao Wu, Deyu Meng, Xiangyong Cao, Weijun Yuan, Zhan Li, Zhan-Pin Chen, Boyang Yao, Yihang Chen, Yifan Deng, Zengyuan Zuo, Junjun Jiang, Saiprasad Meesiyawar, Sulocha Yatageri, Nikhil Neelkanth Akalwadi, R. Tabib, U. Mudenagudi, Jiachen Tu, Yao Shi, Guoyi Xu, Yaoxin Jiang, Cici Liu, Tong Mu, Qiong Cao, Yifan Wang, Kosuke Shigematsu, Hiroto Shirono, Asuka Shin, Wei Zhou, Linfeng Li, Lingdong Kong, Ce Wang, Xing Zhong, Wanjie Sun, Dafeng Zhang, Hongxing Lan, Qisheng Xu, Min He, Hui Geng, Tianjiao Wan, Kele Xu, Chang-yao Wang, A. Carreaud, N. Santacroce, Shanci Li, Jan Skaloud, A. Gressin
/ Abstract
This paper presents the NTIRE 2026 Remote Sensing Infrared Image Super-Resolution (x4) Challenge, one of the associated challenges of NTIRE 2026. The challenge aims to recover high-resolution (HR) infrared images from low-resolution (LR) inputs generated through bicubic downsampling with a x4 scaling factor. The objective is to develop effective models or solutions that achieve state-of-the-art performance for infrared image SR in remote sensing scenarios. To reflect the characteristics of infrared data and practical application needs, the challenge adopts a single-track setting. A total of 115 participants registered for the competition, with 13 teams submitting valid entries. This report summarizes the challenge design, dataset, evaluation protocol, main results, and the representative methods of each team. The challenge serves as a benchmark to advance research in infrared image super-resolution and promote the development of effective solutions for real-world remote sensing applications.