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The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview

Kai Liu
Haoyang Yue
Zeli Lin
Zheng Chen
Jingkai Wang
Jue Gong
Jiatong Li
Xianglong Yan
Libo Zhu
Jianze Li
Ziqing Zhang
Zihan Zhou
Xiaoyang Liu
Radu Timofte
Yulun Zhang
Junye Chen
Zhenming Yan
Yucong Hong
Ruize Han
Song Wang
Li Pang
Heng Zhao
Xinqiao Wu
Deyu Meng
Xiangyong Cao
Weijun Yuan
Zhan Li
Zhanglu Chen
Boyang Yao
Yihang Chen
Yifan Deng
Zengyuan Zuo
Junjun Jiang
Saiprasad Meesiyawar
Sulocha Yatageri
Nikhil Akalwadi
Ramesh Ashok Tabib
Uma Mudenagudi
Jiachen Tu
Yaokun Shi
Guoyi Xu
Yaoxin Jiang
Cici Liu
Tongyao Mu
Qiong Cao
Yifan Wang
Kosuke Shigematsu
Hiroto Shirono
Asuka Shin
Wei Zhou
Linfeng Li
Lingdong Kong
Ce Wang
Xingwei Zhong
Wanjie Sun
Dafeng Zhang
Hongxin Lan
Qisheng Xu
Mingyue He
Hui Geng
Tianjiao Wan
Kele Xu
Changjian Wang
Antoine Carreaud
Nicola Santacroce
Shanci Li
Jan Skaloud
Adrien Gressin
Main:8 Pages
3 Figures
Bibliography:4 Pages
4 Tables
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.

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