Learning A Single Network for Scale-Arbitrary Super-Resolution

• URL: http://arxiv.org/abs/2004.03791v2
• Date: Fri, 23 Jul 2021 14:01:58 GMT
• Title: Learning A Single Network for Scale-Arbitrary Super-Resolution
• Authors: Longguang Wang, Yingqian Wang, Zaiping Lin, Jungang Yang, Wei An, and Yulan Guo
• Abstract summary: We propose to learn a scale-arbitrary image SR network from scale-specific networks. Our plug-in module can be easily adapted to existing networks to achieve scale-arbitrary SR.
• Score: 43.025921944418485
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, the performance of single image super-resolution (SR) has been significantly improved with powerful networks. However, these networks are developed for image SR with a single specific integer scale (e.g., x2;x3,x4), and cannot be used for non-integer and asymmetric SR. In this paper, we propose to learn a scale-arbitrary image SR network from scale-specific networks. Specifically, we propose a plug-in module for existing SR networks to perform scale-arbitrary SR, which consists of multiple scale-aware feature adaption blocks and a scale-aware upsampling layer. Moreover, we introduce a scale-aware knowledge transfer paradigm to transfer knowledge from scale-specific networks to the scale-arbitrary network. Our plug-in module can be easily adapted to existing networks to achieve scale-arbitrary SR. These networks plugged with our module can achieve promising results for non-integer and asymmetric SR while maintaining state-of-the-art performance for SR with integer scale factors. Besides, the additional computational and memory cost of our module is very small.

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