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Scale-Arbitrary Super-Resolution
4K runs

Run time and cost

Predictions run on Nvidia T4 GPU hardware. Predictions typically complete within 20 seconds. The predict time for this model varies significantly based on the inputs.


Pytorch implementation of "Learning A Single Network for Scale-Arbitrary Super-Resolution", ICCV 2021

[Project] [arXiv]


  • A plug-in module to extend a baseline SR network (e.g., EDSR and RCAN) to a scale-arbitrary SR network with small additional computational and memory cost.
  • Promising results for scale-arbitrary SR (both non-integer and asymmetric scale factors) while maintaining the state-of-the-art performance for SR with integer scale factors.


Although recent CNN-based single image SR networks (e.g., EDSR, RDN and RCAN) have achieved promising performance, they are developed for image SR with a single specific integer scale (e.g., x2, x3, x4). In real-world applications, non-integer SR (e.g., from 100x100 to 220x220) and asymmetric SR (e.g., from 100x100 to 220x420) are also necessary such that customers can zoom in an image arbitrarily for better view of details.



  title={Learning A Single Network for Scale-Arbitrary Super-Resolution},
  author={Longguang Wang, Yingqian Wang, Zaiping Lin, Jungang Yang, Wei An, and Yulan Guo},


This code is built on EDSR (PyTorch) and Meta-SR. We thank the authors for sharing the codes.