Image Super-Resolution With Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining

@article{Mei2020ImageSW,
  title={Image Super-Resolution With Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining},
  author={Yiqun Mei and Yuchen Fan and Yuqian Zhou and Lichao Huang and Thomas S. Huang and Humphrey Shi},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020},
  pages={5689-5698}
}
  • Yiqun Mei, Yuchen Fan, Humphrey Shi
  • Published 1 June 2020
  • Computer Science
  • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Deep convolution-based single image super-resolution (SISR) networks embrace the benefits of learning from large-scale external image resources for local recovery, yet most existing works have ignored the long-range feature-wise similarities in natural images. Some recent works have successfully leveraged this intrinsic feature correlation by exploring non-local attention modules. However, none of the current deep models have studied another inherent property of images: cross-scale feature… 

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