An Improved Super-Resolution with Manifold Learning and Histogram Matching

  • Tak Ming Chan
  • Junping Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


Biometric Person Authentication such as face, fingerprint, palmprint and signature depends on the quality of image processing. When it needs to be done under a low-resolution image, the accuracy will be impaired. So how to recover the lost information from downsampled images is important for both authentication and preprocessing. Based on Super-Resolution through Neighbor Embedding algorithm and histogram matching, we propose an improved super-resolution approach to choose more reasonable training images. First, the training image are selected by histogram matching. Second, neighbor embedding algorithm is employed to recover the high-resolution image. Experiments in several images show that our improved super-resolution approach is promising for potential applications such as low-resolution mobile phone or CCTV (Closed Circuit Television) image person authentication.


Training Image Color Histogram Manifold Learning Person Authentication Histogram Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Englewood Cliffs (2002)Google Scholar
  2. 2.
    Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-Based Super-Resolution. In: Proceedings of Computer Graphics and Applications, March/April 2002, pp. 56–65. IEEE, Los Alamitos (2002)Google Scholar
  3. 3.
    Baker, S., Kanade, T.: Limits on Super-Resolution and How to Break Them. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(9) (September 2002)Google Scholar
  4. 4.
    Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Washington, DC, USA, 27 June - 2 July, vol. 1, pp. 275–282 (2004)Google Scholar
  5. 5.
    Zhang, J., Li, S.Z., Wang, J.: Manifold Learning and Applications in Recognition,”in. In: Tan, Y.P., Yap, K.H., Wang, L. (eds.) Intelligent Multimedia Processing with Soft Computing. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Zhang, J.: Several Problmes in Manifold Learning. In: Zhou, Z.-H. (ed.) Machine Learning and Applications. Tsinghua University Press (2005)Google Scholar
  7. 7.
    Roweis, S.T., Lawrance, K.S.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)CrossRefGoogle Scholar
  8. 8.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET Evaluation Methodology for Face Recognition Algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 22, 1090–1104 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Tak Ming Chan
    • 1
  • Junping Zhang
    • 1
    • 2
  1. 1.Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science and EngineeringFudan UniversityChina
  2. 2.The Key Laboratory of Complex Systems and Intelligence Science, Institute of AutomationChinese Academy of SciencesBeijingChina

Personalised recommendations