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Image and Video Super Resolution Techniques

  • Reference work entry
Encyclopedia of Multimedia

  • 267 Accesses

Synonyms

Medical images for medical diagnosis

Definition

Image and video super resolution techniques refer to creating higher resolution image from single/multiple low-resolution input.

Introduction

Images with high pixel density are desirable in many applications, such as high-resolution (HR) medical images for medical diagnosis, high quality video conference, high definition Television broadcasting, Blu-ray movies, etc. While people can use higher resolution camera for the purpose, there is an increasing demand to shoot HR image/video from low-resolution (LR) cameras such as cell phone camera or webcam, or converting existing standard definition footage into high definition video material. Hence, software resolution enhancement techniques are very desirable for these applications.

The task of software image resolution enhancement is to estimate more pixel values to generate a processed image with higher resolution. The simplest way to produce more pixel values is to use up-sampling...

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References

  1. M. Unser, A. Aldroubi, and M. Eden, “Enlargement or reduction of digital images with minimum loss of information,” IEEE Transactions on Image Process, No. 3, Mar. 1995, pp. 247–258.

    Google Scholar 

  2. R. Crochiere and L. Rabiner, “Interpolation and decimation of digital signals – a turorial review,” Proceedings of IEEE, No. 3, pp. 300–331, Mar. 1981.

    Google Scholar 

  3. S.C. Park, M.K. Park, and M.G. Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Processing Magazine, pp. 21–36, 2003.

    Google Scholar 

  4. R. Tsai and T. Huang, “Multiframe image restoration and registration,” Advances in Computer Vision and Image Processing (JAI Press), pp. 317–339, 1984.

    Google Scholar 

  5. S. Borman and R. Stevenson, “Super-resolution from image sequences-a review,” Proc. of Midwest Symposium on Circuits and Systems, pp. 374–378, 1998.

    Google Scholar 

  6. C. Jiji and C. Subhasis, “Single-frame image super-resolution through contourlet learning,” EURASIP Journal on Applied Signal Processing, p. 73767(11), 2006.

    Google Scholar 

  7. M.K. Ng and N.K. Bose, “Analysis of displacement errors in high-resolution image reconstruction with multisensors,” IEEE Transactions on Circuits and Systems (Part I), No. 6, pp. 806–813, 2002.

    Google Scholar 

  8. M.K. Ng and N.K. Bose, “Fast color image restoration with multisensors,” International Journal of Imaging Systems and Technoloy, No. 5, pp. 189–197, 2002.

    Google Scholar 

  9. N. Nguyen, P. Milanfar, and G. Golub, “A computationally efficient super-resolution image reconstruction algorithm,” IEEE Transactions on Image Processing, No. 4, pp. 573–583, 2001.

    Google Scholar 

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

    Google Scholar 

  11. D. Rajan and S. Chaudhuri, “An mrf-based approach to generation of super-resolution images from blurred observations,” Journal of Mathematical Imaging and Vision, No. 1, pp. 5–15, 2002.

    Google Scholar 

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

    Google Scholar 

  13. N. Nguyen, P. Milanfar, and G. Golub, “Efficient generalized cross-validation with applications to parametric image restoration and resolution enhancement,” IEEE Transactions on Image Processing, pp. 1299–1308, Sept. 2001.

    Google Scholar 

  14. M.K. Ng, J. Koo, and N.K. Bose, “Constrained total leastsquares computations for high-resolution image reconstruction with multisensors,” International Journal of Imaging Systems and Technology, No. 1, 2002, pp. 35–42.

    Google Scholar 

  15. S. Borman and R. Stevenson, “Spatial resolution enhancement of low-resolution image sequences. a comprehensive review with directions for future research,” Laboratory Image and Signal Analysis, University of Notre Dame, Technical Report, 1998.

    Google Scholar 

  16. M. Irani and S. Peleg, “Improving resolution by image registration,” CVGIP: Graphical Models and Image Processing, No. 3, 1991, pp. 231–239.

    Google Scholar 

  17. M. Irani and S. Peleg, “Motion analysis for image enhancement: resolution, occlusion, and transparency,” Journal of Visual Communication and Image Representation, No. 4, 1993, p. 324–335.

    Google Scholar 

  18. W.T. Freeman, E.C. Pasztor, and O.T. Carmichael., “Learning low-level vision,” IJCV, No. 1, 2000, pp. 25–47.

    Google Scholar 

  19. J. Sun, H. Tao, and H. Shum, “Image hallucination with primal sketch priors,” Proceedings of the IEEE CVPR’03, pp. 729–736, 2003.

    Google Scholar 

  20. C.V. Jiji, M.V. Joshi, and S. Chaudhuri, “Single-frame image super-resolution using learned wavelet coefficients,” International Journal of Imaging Systems and Technology, No. 3, 2004, pp. 105–112.

    Google Scholar 

  21. D. Capel and A. Zisserman, “Super-resolution from multiple views using learnt image models,” Proceedings of the IEEE CVPR’01, pp. 627–634, December 2001.

    Google Scholar 

  22. Q. Wang, X. Tang, and H. Shum, “Patch based blind image super resolution,” Proceedings of ICCV’05, No. 1, pp. 709–716, 2005.

    Google Scholar 

  23. D.Y.H. Chang and Y. Xiong, “Super-resolution through neighbor embedding,” Proceedings of CVPR’04, 2004, pp. 275–282.

    Google Scholar 

  24. B. Gunturk, A. Batur, Y. Altunbasak, M. Hayes, and R.M. Mersereau, “Eigenface-domain super-resolution for face recognition,” IEEE Transactions on Image Processing, no. 5, 2003, pp. 597–606.

    Google Scholar 

  25. X. Wang and X. Tang, “Hallucinating face by eigentransformation with distortion reduction,” Proceedings of ICBA’04, pp. 88–94, 2004.

    Google Scholar 

  26. C.V. Jiji and S. Chaudhuri, “Pca-based generalized interpolation for image super-resolution,” Proceedings of Indian Conference on Vision, Graphics & Image Processing’04, pp. 139–144, 2004.

    Google Scholar 

  27. G. Dalley, B. Freeman, and J. Marks, “Single-frame text super-resolution: a bayesian approach,” Proceedings of IEEE ICIP’04, pp. 3295–3298, 2004.

    Google Scholar 

  28. D. Kong, M. Han, W. Xu, H. Tao, and Y. Gong, “A conditional random field model for video super-resolution,” Proceedings of ICPR’06, pp. 619–622, 2006.

    Google Scholar 

  29. Z. Lin and H. Shum, “Fundamental limits of reconstruction-based super-resolution algorithms under local translation,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), no. 1, 2004, pp. 83–97.

    Google Scholar 

  30. M. Ben-Ezra, A. Zomet, and S. Nayar, “Jitter camera: High resolution video from a low resolution detector,” Proceedings of IEEE CVPR’04, pp. 135–142, Jun. 2004.

    Google Scholar 

  31. C. Bishop, A. Blake, and B. Marthi, “Super-resolution enhancement of video,” Proceedings of the Artificial Intelligence and Statistics, 2003.

    Google Scholar 

  32. C. Williams and C. Rasmussen, “Gaussian processes for regression,” Advances in Neural Information Processing Systems, MIT Press., Cambridge, MA, 1996, pp. 514–520.

    Google Scholar 

  33. S. Dai, M. Han, W. Xu, Y. Wu, and Y. Gong, “Soft edge smoothness prior for alpha channel super resolution,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, 2007.

    Google Scholar 

  34. R. Hardie, K. Barnard, and J. Bognar, “High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Optical Engineering, No. 1, Jan. 1998, pp. 247–260.

    Google Scholar 

  35. S. Farsiu, M.Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Transactions on Image Processing, pp. 1327–1344, 2004.

    Google Scholar 

  36. Levin, D. Lischinski, and Y. Weiss, “A closed form solution to natural image matting,” Proceedings of the IEEE CVPR’06, 2006.

    Google Scholar 

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Author information

Authors and Affiliations

  1. NEC Laboratories America, Inc., Cupertino, CA, USA

    Jinjun Wang & Yihong Gong

Authors
  1. Jinjun Wang
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  2. Yihong Gong
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Florida Atlantic University (FAU), 33431, Boca Raton, FL, USA

    Borko Furht (Department Chair) (Department Chair)

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© 2008 Springer-Verlag

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Wang, J., Gong, Y. (2008). Image and Video Super Resolution Techniques. In: Furht, B. (eds) Encyclopedia of Multimedia. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-78414-4_27

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