A Bayesian Approach to Alignment-Based Image Hallucination

  • Marshall F. Tappen
  • Ce Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7578)


In most image hallucination work, a strong assumption is held that images can be aligned to a template on which the prior of high-res images is formulated and learned. Realizing that one template can hardly generalize to all images of an object such as faces due to pose and viewpoint variation as well as occlusion, we propose an example-based prior distribution via dense image correspondences. We introduce a Bayesian formulation based on an image prior that can implement different effective behaviors based on the value of a single parameter. Using faces as examples, we show that our system outperforms the prior state of art.


Input Image Bayesian Approach Super Resolution Bicubic Interpolation SSIM Index 
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  1. 1.
    Sun, J., Sun, J., Xu, Z.B., Shum, H.Y.: Image super-resolution using gradient profile prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  2. 2.
    Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 1167–1183 (2002)CrossRefGoogle Scholar
  3. 3.
    Fattal, R.: Upsampling via imposed edges statistics. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2007) 26 (2007)Google Scholar
  4. 4.
    Greenspan, H., Anderson, C.H., Akber, S.: Image enhancement by nonlinear extrapolation in frequency space. IEEE Transactions on Image Processing 9, 1035–1048 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. International Journal of Computer Vision 40, 25–47 (2000)zbMATHCrossRefGoogle Scholar
  6. 6.
    Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Processing 19, 2861–2873 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Sun, J., Zhu, J., Tappen, M.F.: Context-constrained hallucination for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 231–238 (2010)Google Scholar
  8. 8.
    Tai, Y.W., Liu, S., Brown, M.S., Lin, S.: Super resolution using edge prior and single image detail synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2400–2407 (2010)Google Scholar
  9. 9.
    Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: ICCV (2009)Google Scholar
  10. 10.
    HaCohen, Y., Fattal, R., Lischinski, D.: Image upsampling via texture hallucination. In: Proceedings of the International Conference on Computational Pohotography (2010)Google Scholar
  11. 11.
    Liu, C., Shum, H.Y., Freeman, W.T.: Face hallucination: Theory and practice. International Journal of Computer Vision 75, 115–134 (2007)CrossRefGoogle Scholar
  12. 12.
    Liu, C., Yuen, J., Torralba, A.: Sift flow: Dense correspondence across scenes and its applications. IEEE Trans. Pattern Anal. Mach. Intell. 33, 978–994 (2011)CrossRefGoogle Scholar
  13. 13.
    Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: PatchMatch: A randomized correspondence algorithm for structural image editing. ACM Transactions on Graphics (Proc. SIGGRAPH) 28 (2009)Google Scholar
  14. 14.
    HaCohen, Y., Shechtman, E., Goldman, D.B., Lischinski, D.: Non-rigid dense correspondence with applications for image enhancement. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH 2011) 30, 70:1–70:9 (2011)Google Scholar
  15. 15.
    Yang, F., Wang, J., Shechtman, E., Bourdev, L.D., Metaxas, D.N.: Expression flow for 3d-aware face component transfer. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH 2011) 30, 60 (2011)Google Scholar
  16. 16.
    Simakov, D., Caspi, Y., Shechtman, E., Irani, M.: Summarizing visual data using bidirectional similarity. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  17. 17.
    Pinto, N., Stone, Z., Zickler, T., Cox, D.D.: Scaling-up Biologically-Inspired Computer Vision: A Case-Study on Facebook. In: IEEE Computer Vision and Pattern Recognition, Workshop on Biologically Consistent Vision (2011)Google Scholar
  18. 18.
    Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and simile classifiers for face verification. In: Proceedings of the IEEE International Conference on Computer Vision (2009)Google Scholar
  19. 19.
    Freeman, W.T., Liu, C.: Markov random fields for super-resolution and texture synthesis. In: Advances in Markov Random Fields for Vision and Image Processing. MIT Press (2011)Google Scholar
  20. 20.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13, 600–612 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marshall F. Tappen
    • 1
  • Ce Liu
    • 2
  1. 1.University of Central FloridaUSA
  2. 2.Microsoft Research New EnglandUK

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