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An MRF-Based Approach to Generation of Super-Resolution Images from Blurred Observations

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Abstract

This paper presents a new technique for generating a high resolution image from a blurred image sequence; this is also referred to as super-resolution restoration of images. The image sequence consists of decimated, blurred and noisy versions of the high resolution image. The high resolution image is modeled as a Markov random field (MRF) and a maximum a posteriori (MAP) estimation technique is used for super-resolution restoration. Unlike other super-resolution imaging methods, the proposed technique does not require sub-pixel registration of given observations. A simple gradient descent method is used to optimize the functional. The discontinuities in the intensity process can be preserved by introducing suitable line processes. Superiority of this technique to standard methods of image expansion like pixel replication and spline interpolation is illustrated.

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References

  1. B. Bascle, A. Blake, and A. Zissermann, “Motion deblurring and super-resolution from an image sequence,” in Proc. of European Conf. on Computer Vision, Cambridge, UK, 1996, Springer-Verlag: Berlin.

    Google Scholar 

  2. A. Blake and A. Zisserman, Visual Reconstruction, MIT Press: Cambridge, MA, 1987.

    Google Scholar 

  3. S. Chaudhuri and A.N. Rajagopalan, Depth from Defocused Images: A Real Aperture Imaging Approach, Springer-Verlag: New York, 1999.

    Google Scholar 

  4. P. Cheeseman, B. Kanefsky, R. Kraft, J. Stutz, and R. Hanson, “Super-resolved surface reconstruction from multiple images,” NASA Ames Research Center, Moffet Field, CA. Technical Report FIA-94-12, 1994.

    Google Scholar 

  5. M.-C. Chiang and T.E. Boult, “Local blur estimation and superresolution,” in Proc. IEEE Conf. Computer Visin and Pattern Recognition, Puerto Rico, USA, 1997, pp. 821–826.

  6. M. Elad and A. Feuer, “Restoration of a single super-resolution image from several blurred, noisy and undersampled measured images,” Dept. of Electrical Engg,Technion, Israel Instt. ofTechnology, Technical Report EE Pub No. 967, 1995.

  7. M. Elad and A. Feuer, “Restoration of a single super-resolution image from several blurred, noisy and undersampled measured images,” IEEE Trans. on Image Processing, Vol. 6, No. 12, pp. 1646–1658, 1997.

    Google Scholar 

  8. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distribution and the Bayesian restoration of image,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 6, No. 6, pp. 721–741, 1984.

    Google Scholar 

  9. J. Hadamard, Lectures on the Cauchy Problem in Linear Partial Differential Equations, Yale University Press: New Haven, CT, 1923.

    Google Scholar 

  10. M. Irani and S. Peleg, “Improving resolution by image registration,” CVGIP:Graphical Models and Image Processing,Vol. 53, pp. 231–239, 1991.

    Google Scholar 

  11. M. Irani and S. Peleg, “Motion analysis for image enhancement: Resolution, occlusion and transparency,” Journal of VCIR, Vol. 4, pp. 324–335, 1993.

    Google Scholar 

  12. S.P. Kim, N.K. Bose, and H.M. Valenzuela, “Recursive reconstruction of high resolution image from noisy undersampled multiframes,” IEEE Trans. on Accoustics, Speech and Signal Processing, Vol. 18, No. 6, pp. 1013–1027, 1990.

    Google Scholar 

  13. S.P. Kim and W.-Y. Su, “Recursive high-resolution reconstruction of blurred multiframe images,” IEEE Trans. on Image Processing, Vol. 2, pp. 534–539, 1993.

    Google Scholar 

  14. S. Krishnamachari and R. Chellappa, “Multiresolution Gauss-Markov random field models for texture segmentation,” IEEE Trans. on Image Processing, Vol. 6, No. 2, pp. 251–266, 1997.

    Google Scholar 

  15. S.Z. Li, Markov Random Field Modelling in Computer Vision, Springer-Verlag: Tokyo, 1995.

    Google Scholar 

  16. A. Papoulis, “Generalized sampling theorem,” IEEE Trans. on Circuits and Systems, Vol. 24, pp. 652–654, 1977.

    Google Scholar 

  17. A.J. Patti, M.I. Sezan, and A.M. Tekalp, “High resolution image reconstruction from a low resolution image sequence in the MRF-Based Approach to Generation of Super-Resolution Images 15 presence of time-varying motion blur,” in Proc. ICIP, Austin, USA, 1994, pp. 343–347.

  18. A.N. Rajagopalan and S. Chaudhuri, “Space-variant approaches to recovery of depth from defocused images,” Computer Vision and Image Understanding, Vol. 68, No. 3, pp. 309–329, 1997.

    Google Scholar 

  19. D. Rajan and S. Chaudhuri, “Ageneralized interpolation scheme for image scaling and super-resolution,” in Proc. of Erlangen Workshop 99 on Vision, Modelling and Visualization, University of Erlangen-Nuremberg, Germany, Nov. 1999, pp. 301–308.

  20. C.H. Russel, K.J. Barnard, and E.E. Armstrong, “Joint MA Pregistration and high resolution image estimation using a sequence of undersampled images,” IEEE Trans. on Image Processing, Vol. 6, No. 12, pp. 1621–1633, 1997.

    Google Scholar 

  21. C.H. Russel, K.J. Barnard, J.G. Bognar, E.E. Armstrong, and E.A. Watson, “Joint high resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Optical Engineering, Vol. 37, No. 1, pp. 247–260, 1998.

    Google Scholar 

  22. R.R. Schultz and R.L. Stevenson, “A Bayesian approach to image expansion for improved definition,” IEEE Trans. on Image Processing, Vol. 3, No. 3, pp. 233–242, 1994.

    Google Scholar 

  23. R.R. Schultz and R.L. Stevenson, “Extraction of high-resolution frames from video sequences,” IEEE Trans. on Image Processing, Vol. 5, pp. 996–1011, 1996.

    Google Scholar 

  24. H. Shekarforoush, M. Berthod, and J. Zerubia, “3D superresolution using generalized sampling expansion,” in Proc. Int. Conf. on Image Processing, Washington D.C., 1995, pp. 300–303.

  25. H. Shekarforoush, M. Berthod, J. Zerubia, and M. Werman, “Sub-pixel Bayesian estimation of albedo and height,” International Journal of Computer Vision, Vol. 19, No. 3, pp. 289–300, 1996.

    Google Scholar 

  26. C. Srinivas and M.D. Srinath, “A stochastic model based approach for simultaneous restoration of multiple mis-registered images,” SPIE, Vol. 1360, pp. 1416–1427, 1990.

    Google Scholar 

  27. A.M. Tekalp, M.K. Ozkan, and M.I. Sezan, “High resolution image reconstruction from lower-resolution image sequences and space-varying image restoration,” in Proc. ICAASP, San Francisco, USA, 1992, pp. 169–172.

  28. B.C. Tom and A.K. Katsaggelos, “Reconstruction of a highresolution image by simultaneous registration, restoration and interpolation of low-resolution images,” in Proc. of Int Conf. Image Processing, Washington D.C., 1995, pp. 539–542.

  29. R.Y. Tsai and T.S. Huang, “Multiframe image restoration and registration,” in Advances in Computer Vision and Image Processsing, JAI Press: London, 1984, pp. 317–339.

    Google Scholar 

  30. H. Ur and D. Gross, “Improved resolution from sub-pixel shifted pictures,” CVGIP:Graphical Models and Image Processing, Vol. 54, pp. 181–186, 1992.

    Google Scholar 

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Authors and Affiliations

  1. School of Biomedical Engineering, Indian Institute of Technology-Bombay, Powai, Mumbai, 400076, India

    Deepu Rajan

  2. Department of Electrical Engineering, Indian Institute of Technology-Bombay, Powai, Mumbai, 400076, India

    Subhasis Chaudhuri

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  1. Deepu Rajan
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  2. Subhasis Chaudhuri
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Rajan, D., Chaudhuri, S. An MRF-Based Approach to Generation of Super-Resolution Images from Blurred Observations. Journal of Mathematical Imaging and Vision 16, 5–15 (2002). https://doi.org/10.1023/A:1013961817285

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