The Eleventh NTIRE 2026 Efficient Super-Resolution Challenge Report
Bin Ren, Hang Guo, Yan Shu, Jiaqi Ma, Ziteng Cui, Shuhong Liu, Guofeng Mei, Lei Sun, Zongwei Wu, Fahad Shahbaz Khan, Salman Khan, Radu Timofte, Yawei Li, Hongyuan Yu, Pufan Xu, Chen Wu, Long Peng, Jiaojiao Yi, Siyang Yi, Yuning Cui, Jingyuan Xia, Xing Mou, Keji He, Jinlin Wu
TL;DR
This paper reports on the NTIRE 2026 challenge focused on developing efficient super-resolution networks that balance performance with computational cost, highlighting the state-of-the-art solutions and results.
Contribution
It presents the challenge setup, participant results, and insights into effective strategies for efficient super-resolution models.
Findings
95 teams registered, 15 submitted valid solutions
Achieved PSNR around 26.9 dB on benchmark datasets
Identified effective methods for reducing runtime, parameters, and FLOPs
Abstract
This paper reviews the NTIRE 2026 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects, such as runtime, parameters, and FLOPs, while maintaining PSNR of around 26.90 dB on the DIV2K_LSDIR_valid dataset, and 26.99 dB on the DIV2K_LSDIR_test dataset. The challenge had 95 registered participants, and 15 teams made valid submissions. They gauge the state-of-the-art results for efficient single-image super-resolution.
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