This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution (ESR). The challenge aimed to advance the development of deep models that optimize key computational metrics, i.e., runtime, parameters, and FLOPs, while achieving a PSNR of at least 26.90 dB on the DIV2K_LSDIR_valid dataset and 26.99 dB on the DIV2K_LSDIR_test dataset. A robust participation saw 244 registered entrants, with 43 teams submitting valid entries. This report meticulously analyzes these methods and results, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques. The analysis highlights innovative approaches and establishes benchmarks for future research in the field.
Ren, B., Guo, H., Sun, L., Wu, Z., Timofte, R., Li, Y., et al. (2025). The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp.908-957). IEEE Computer Society [10.1109/CVPRW67362.2025.00095].
The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report
Bianco S.;Cogo L.;
2025
Abstract
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution (ESR). The challenge aimed to advance the development of deep models that optimize key computational metrics, i.e., runtime, parameters, and FLOPs, while achieving a PSNR of at least 26.90 dB on the DIV2K_LSDIR_valid dataset and 26.99 dB on the DIV2K_LSDIR_test dataset. A robust participation saw 244 registered entrants, with 43 teams submitting valid entries. This report meticulously analyzes these methods and results, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques. The analysis highlights innovative approaches and establishes benchmarks for future research in the field.| File | Dimensione | Formato | |
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Ren-2025-CVPRW 2025-AAM.pdf
accesso aperto
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Author’s Accepted Manuscript, AAM (Post-print)
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Licenza open access specifica dell’editore
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9.85 MB
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