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This paper summarizes the NTIRE 2026 challenge on 4x super-resolution of remote sensing infrared images, using bicubic downsampling to generate low-resolution inputs. The challenge provides a benchmark dataset and evaluation protocol to advance infrared image SR research. Thirteen teams submitted valid entries, and the paper details their methods and results, establishing a performance baseline for future work.
A new benchmark dataset and challenge results highlight the state-of-the-art in infrared image super-resolution for remote sensing, pushing the field towards practical applications.
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.
