SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation

@article{Son2021SRWarpGI,
  title={SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation},
  author={Sanghyun Son and Kyoung Mu Lee},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={7778-7787}
}
  • Sanghyun SonKyoung Mu Lee
  • Published 21 April 2021
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Deep CNNs have achieved significant successes in image processing and its applications, including single image super-resolution (SR). However, conventional methods still resort to some predetermined integer scaling factors, e.g., ×2 or ×4. Thus, they are difficult to be applied when arbitrary target resolutions are required. Recent approaches ex-tend the scope to real-valued upsampling factors, even with varying aspect ratios to handle the limitation. In this pa-per, we propose the SRWarp… 

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