Edge Model Based High Resolution Image Generation

  • Malay Kumar Nema
  • Subrata Rakshit
  • Subhasis Chaudhuri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


The present paper proposes a new method for high resolution image generation from a single image. Generation of high resolution (HR) images from lower resolution image(s) is achieved by either reconstruction-based methods or by learning-based methods. Reconstruction based methods use multiple images of the same scene to gather the extra information needed for the HR. The learning-based methods rely on the learning of characteristics of a specific image set to inject the extra information for HR generation. The proposed method is a variation of this strategy. It uses a generative model for sharp edges in images as well as descriptive models for edge representation. This prior information is injected using the Symmetric Residue Pyramid scheme. The advantages of this scheme are that it generates sharp edges with no ringing artefacts in the HR and that the models are universal enough to allow usage on wide variety of images without requirement of training and/or adaptation. Results have been generated and compared to actual high resolution images.

Index terms: Super-Resolution, edge modelling, Laplacian pyramids.


Sharp Edge Ringing Artefact Bicubic Interpolation Laplacian Pyramid Edge Model 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ur, H., Gross, D.: Improved resolution from sub-pixel shifted pictures. CVGIP:Graphical Models and Image Processing 54, 181–186 (1992)CrossRefGoogle Scholar
  2. 2.
    Nguyen, N., Milanfar, P.: An efficient wavelet-based algorithm for imaqe superresolution. In: Proc. Int. Conf. Image Processing, vol. 2, pp. 351–354 (2000)Google Scholar
  3. 3.
    Tsai, R., Huang, T.: Multiple frame image restoration and registration. In: Advances in Computer and Image Processing, pp. 317–339. JAI Press Inc., CT (1984)Google Scholar
  4. 4.
    Rhee, S., Kang, M.: Discrete cosine transform based regularized high-resolution image reconstruction algorithm. Opt. Eng. 38, 1348–1356 (1999)CrossRefGoogle Scholar
  5. 5.
    Chaudhuri, S. (ed.): Super-Resolution Imaging. Kluwer Academic, Norwell (2001)Google Scholar
  6. 6.
    Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: A technical overview. IEEE Signal Processing Magazine, 21–36 (2003)Google Scholar
  7. 7.
    Joshi, M., Chaudhuri, S.: Super-resolution imaging: Use of zoom as a cue. In: Proc. ICVGIP, Ahmedabad, India (2002)Google Scholar
  8. 8.
    Joshi, M., Chaudhuri, S.: Zoom based super-resolution through sar model fitting. In: Proc.Intl Conf. on Image Processing (ICIP), Singapore (October 2004)Google Scholar
  9. 9.
    Jiji, C.V., Joshi, M.V., Chaudhuri, S.: Single frame image super-resolution using learnt wavelet coefficients. Intl. J. Imaging Science & Tech (special issue on high resolution image reconstruction) 14, 105–112 (2004)Google Scholar
  10. 10.
    Jiji, C.V., Joshi, M.V., Chaudhuri, S.: Single frame image super-resolution through contourlet learning. EURASIP J. Applied Signal Processing, 1–11 (2006)Google Scholar
  11. 11.
    Chaudhuri, S., Joshi, M.: Motion-Free Super-Resolution. Springer, HeidelbergGoogle Scholar
  12. 12.
    Burt, P., Adelson, E.: The laplacian pyramid as a compact image code. IEEE Trans. Commuication 31, 532–540 (1983)CrossRefGoogle Scholar
  13. 13.
    Nema, M.K., Rakshit, S.: Edge-model based representation of laplacian subbands. In: Proc. Seventh Asian Conf. on Computer Vision (ACCV 7), Hyderabad, India, January 2006, pp. 80–89 (2006)Google Scholar
  14. 14.
    Rakshit, S., Nema, M.K.: Symmetric residue pyramids: An extension to burt laplacian pyramids. In: Proc. IEEE ICASSP, Hong Kong, pp. III–317–III–320 (2003)Google Scholar
  15. 15.
    Elad, M., Feuer, A.: Restoration of a single superresolution image from several blurred, noisy and undersampled measured images. IEEE Trans. Image Processing 6, 1646–1658 (1997)CrossRefGoogle Scholar
  16. 16.
    Naguyen, N., Milanfar, P., Golub, G.: Efficient generalizd cross-validation with applications to parameteric image restortion and resolution enhancement. IEEE Trans. Image Processing 10, 1299–1308 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Malay Kumar Nema
    • 1
  • Subrata Rakshit
    • 1
  • Subhasis Chaudhuri
    • 2
  1. 1.Centre for Artificial Intelligence and RoboticsBangalore
  2. 2.VIP Lab, Department of Electrical EngineeringIIT BombayMumbai

Personalised recommendations