Super-Sampling by Learning-Based Super-Resolution

  • Ping Du
  • Jinhuan Zhang
  • Jun LongEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11338)


In this paper, we present a novel problem of intelligent image processing, which is how to infer a finer image in terms of intensity levels for a given image. We explain the motivation for this effort and present a simple technique that makes it possible to apply the existing learning-based super-resolution methods to this new problem. As a result of the adoption of the intelligent methods, the proposed algorithm needs notably little human assistance. We also verify our algorithm experimentally in the paper.


Texture synthesis Super-resolution Image manifold 


  1. 1.
    Freeman, H.: Computer processing of line-drawing images. ACM Comput. Surv. (CSUR) 6(1), 57–97 (1974)CrossRefGoogle Scholar
  2. 2.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems. Curran Associates Inc., pp. 1097–1105 (2012)Google Scholar
  3. 3.
    Yuan, C., Li, X., Wu, Q.M.J., Li, J., Sun, X.: Fingerprint liveness detection from different fingerprint materials using convolutional neural network and principal component analysis. CMC Comput. Mater. Continua 53(3), 357–371 (2017)Google Scholar
  4. 4.
    Li, Y., Wang, G., Nie, L., Wang, Q.: Distance metric optimization driven convolutional neural network for age invariant face recognition. Pattern Recogn. Scholar
  5. 5.
    Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: International Conference on Neural Information Processing Systems, pp. 2672–2680. MIT Press (2014)Google Scholar
  6. 6.
    Narang, N., Bourlai, T.: Face recognition in the SWIR band when using single sensor multi-wavelength imaging systems. Image Vis. Comput. 33(1), 26–43 (2015)CrossRefGoogle Scholar
  7. 7.
    Feng, K., Zhou, T., Cui, J., et al.: An example image super-resolution algorithm based on modified k-means with hybrid particle swarm optimization. In: Proceedings of the SPIE/COS Photonics Asia. International Society for Optics and Photonics, pp. 1–11 (2014)Google Scholar
  8. 8.
    Farokhi, S., Shamsuddin, S.M., Sheikh, U., et al.: Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform. Digital Signal Process. 31(1), 13–27 (2014)CrossRefGoogle Scholar
  9. 9.
    Biswas, S., Aggarwal, G., Flynn, P.J., et al.: Pose-robust recognition of low-resolution face images. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 3037–3049 (2013)CrossRefGoogle Scholar
  10. 10.
    Chang, H., Yeung, D.-Y., Xiong, Y.: Super-resolution through neighbor embedding. In: 2004 CVPR 2004 Proceedings of the 2004 IEEE Computer Society Conference on Proceedings of the Computer Vision and Pattern Recognition, pp. I–8. IEEE (2004)Google Scholar
  11. 11.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  12. 12.
    Wang, N., Tao, D., Gao, X., et al.: A comprehensive survey to face hallucination. Int. J. Comput. Vis. 106(1), 9–30 (2014)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina

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