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README.md

Learning for Scale-Arbitrary Super-Resolution from Scale-Specific Networks

[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.
  • 70 epochs to train the extended network by using a scale-aware knowledge transfer paradigm to transfer knowledge from scale-specific networks.
  • 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.

Demo

gif

Motivation

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.

We investigate the relationship between x2/x3/x4 SR tasks and observe that the scale-dependency of features in the backbone module is different for different blocks and regions. Motivated by this observation, we distinguish scale-dependent features from scale-independent ones, and then perform scale-aware feature adaption adaptively.

visualization mask

Overview

non-integer

Visual Results

non-integer

asymmetric

Citation

@article{Wang2020Learning,
  title={Learning for Scale-Arbitrary Super-Resolution from Scale-Specific Networks},
  author={Longguang Wang, Yingqian Wang, Zaiping Lin, Jungang Yang, Wei An, and Yulan Guo},
  journal={arXiv},
  year={2020}
}

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A single model for SR with arbitrary scale factors.

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