Deep Back-Projection Networks for Super-Resolution

@article{Haris2018DeepBN,
  title={Deep Back-Projection Networks for Super-Resolution},
  author={M. Haris and Gregory Shakhnarovich and N. Ukita},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={1664-1673}
}
  • M. Haris, Gregory Shakhnarovich, N. Ukita
  • Published 2018
  • Computer Science
  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • The feed-forward architectures of recently proposed deep super-resolution networks learn representations of low-resolution inputs, and the non-linear mapping from those to high-resolution output. [...] Key Method We construct mutually-connected up- and down-sampling stages each of which represents different types of image degradation and high-resolution components. We show that extending this idea to allow concatenation of features across up- and downsampling stages (Dense DBPN) allows us to reconstruct further…Expand Abstract
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    References

    SHOWING 1-10 OF 54 REFERENCES
    Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
    • 839
    • Highly Influential
    • PDF
    Image Super-Resolution Using Deep Convolutional Networks
    • 3,161
    • Highly Influential
    • PDF
    Enhanced Deep Residual Networks for Single Image Super-Resolution
    • 1,413
    • Highly Influential
    • PDF
    Image Super-Resolution via Deep Recursive Residual Network
    • Ying Tai, Jian Yang, X. Liu
    • Computer Science
    • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    • 2017
    • 731
    • Highly Influential
    • PDF
    Deeply-Recursive Convolutional Network for Image Super-Resolution
    • 1,069
    • Highly Influential
    • PDF
    Inception learning super-resolution.
    • 6
    Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
    • W. Shi, J. Caballero, +5 authors Zehan Wang
    • Computer Science, Mathematics
    • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    • 2016
    • 1,714
    • Highly Influential
    • PDF
    Video Super-Resolution via Deep Draft-Ensemble Learning
    • 144
    • PDF
    Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
    • C. Ledig, L. Theis, +6 authors W. Shi
    • Computer Science, Mathematics
    • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    • 2017
    • 4,012
    • PDF
    A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution
    • 939
    • PDF