SuperResolution Image Reconstruction Using a Hybrid Bayesian Approach

  • Tao Wang
  • Yan Zhang
  • Yong Sheng Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


There are increasing demands for high-resolution (HR) images in various applications. Image superresolution (SR) reconstruction refers to methods that increase image spatial resolution by fusing information from either a sequence of temporal adjacent images or multi-source images from different sensors. In the paper we propose a hybrid Bayesian method for image reconstruction, which firstly estimates the unknown point spread function(PSF) and an approximation for the original ideal image, and then sets up the HMRF image prior model and assesses its tuning parameter using maximum likelihood estimator, finally computes the regularized solution automatically. Hybrid Bayesian estimates computed on actual satellite images and video sequence show dramatic visual and quantitative improvements in comparison with the bilinear interpolation result, the projection onto convex sets (POCS) estimate and Maximum A Posteriori (MAP) estimate.


Point Spread Function Bilinear Interpolation Blind Deconvolution Newton Direction SuperResolution Image 
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  1. 1.
    Park, S.C., Park, M.K., Kang, M.G.: Super-Resolution Image Reconstruction: A Technical Overview. IEEE Signal Processing Magazine 5, 21–36 (2003)CrossRefGoogle Scholar
  2. 2.
    Tsai, R.Y., Huang, T.S.: Multiframe image restoration and registration. In: Huang, T.S. (ed.) Advances in computer vision and image processing, pp. 317–339. JAI Press (1984)Google Scholar
  3. 3.
    Patti, A.J., Sezan, M.I., Tekalp, A.M.: Superresolution Video Reconstruction with Arbitrary Sampling Lattices and Nonzereo Aperture Time. IEEE Trans. Image Processing 8, 1064–(1997)CrossRefGoogle Scholar
  4. 4.
    Patti, A.J., Altunbasak, Y.: Artifact reduction for set theoretic super resolution image reconstruction with edge adaptive constraints and higher-order interpolants. IEEE Trans. Image Processing 1, 179–186 (2001)CrossRefGoogle Scholar
  5. 5.
    Schulz, R.R., Stevenson, R.L.: Extraction of High-Resolution Frames from Video Sequences. IEEE Trans. Image Processing 6, 996–1011 (1996)CrossRefGoogle Scholar
  6. 6.
    Hardie, R.C., Barnard, K.J., Armstrong, E.E.: Joint MAP registration and high-resolution image estimation using a sequence of undersampled images. IEEE Trans. Image Processing 12, 1621–1633 (1997)CrossRefGoogle Scholar
  7. 7.
    Jalobeanu, A., Blanc-Féraud, L., et al.: An Adaptive Gaussian Model for Satellite Image deblurring. IEEE Trans. Image Processing 4, 613–621 (2004)CrossRefGoogle Scholar
  8. 8.
    Carasso, A.S.: THE APEX Method in Image Sharpening and the use of low exponent Lévy Stable Laws. SIAM J. Appl. Math. 2, 593–618 (2002)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tao Wang
    • 1
  • Yan Zhang
    • 1
  • Yong Sheng Zhang
    • 1
  1. 1.Zhengzhou Institute of Surveying and MappingZhengzhouChina

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