Image Super-Resolution, a State-of-the-Art Review and Evaluation

Chapter

Abstract

Image super-resolution is a popular technique for increasing the resolution of a given image. Its most common application is to provide better visual effect after resizing a digital image for display or printing. In recent years, due to consumer multimedia products being in vogue, imaging and display device become ubiquitous, and image super-resolution is becoming more and more important. There are mainly three categories of approaches for this problem: interpolation-based methods, reconstruction-based methods, and learning-based methods.

This chapter is aimed, first, to explain the objective of image super-resolution, and then to describe the existing methods with special emphasis on color super-resolution. Finally, the performance of these methods is studied by carrying on objective and subjective image quality assessment on the super-resolution images.

Keywords

Image super-resolution Color super-resolution Interpolation-based methods Reconstruction-based methods Learning-based methods Streaming video websites HDTV displays Digital cinema 

References

  1. 1.
    Borman S, Stevenson RL (1998) Super-resolution from image sequences – A Review. Midwest Symp Circ Syst, 374–378Google Scholar
  2. 2.
    Park SC, Park MK, Kang MG (2003) Super-resolution image reconstruction: a technical overview. IEEE Signal Process Mag 20(3):21–36CrossRefGoogle Scholar
  3. 3.
    Farsiu S, Robinson D, Elad M, Milanfar P (2004) Advances and challenges in super-resolution. Int J Imag Syst Tech 14(2):47–57CrossRefGoogle Scholar
  4. 4.
    Li X, Orchard MT (2001) New edge-directed interpolation. IEEE Trans Image Process 10(10):1521–1527CrossRefGoogle Scholar
  5. 5.
    Blu T Thevenaz P, Unser M (2000) Image interpolation and resampling. Handbook of medical imaging, processing and analysis. Academic, San DiegoGoogle Scholar
  6. 6.
    Jensen K, Anastassiou D (1995) Subpixel edge localization and the interpolation of still images. IEEE Trans Image Process 4:285–295CrossRefGoogle Scholar
  7. 7.
    Allebach J, Wong PW (1996) Edge-directed interpolation. Proc IEEE Int Conf Image Proc 3:707–710Google Scholar
  8. 8.
    Muresan DD, Parks TW (2000) Prediction of image detail. Proc IEEE Int Conf Image Proc, 323–326Google Scholar
  9. 9.
    Chang DB Carey WK, Hermami SS (1999) Regularity-preserving image interpolation. Proc IEEE Int Conf Image Proc, 1293–1297Google Scholar
  10. 10.
    Irani M, Peleg S (1991) Improving resolution by image registration. CVGIP: Graph Models Image Process 53:231–239CrossRefGoogle Scholar
  11. 11.
    Shah NR, Zakhor A (1999) Resolution enhancement of color video sequence. IEEE Trans Image Process 6(8):879–885CrossRefGoogle Scholar
  12. 12.
    Tom BC, Katsaggelos A (2001) Resolution enhancement of monochrome and color video using motion compensation. IEEE Trans Image Process 2(10):278–287CrossRefGoogle Scholar
  13. 13.
    Maalouf A, Larabi MC (2009) Grouplet-based color image super-resolution. EUSIPCO2009, 17th European signal processing conference, Glasgow, ScotlandGoogle Scholar
  14. 14.
    Mallat S (2009) Geometrical grouplets. Appl Comput Harmon Anal 26(2):161–180MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    DiZenzo S (1986) A note on the gradient of multi images. Comput Vis Graph Image Process 33(1):116–125CrossRefGoogle Scholar
  16. 16.
    Hardie R, Barnard K, Amstrong E (1997) Joint map registration and high-resolution image estimation using a sequence of undersampled images. IEEE Trans Image Process 6 (12):1621–1633CrossRefGoogle Scholar
  17. 17.
    Elad M, Feuer A (1997) Restoration of single super-resolution image from several blurred, noisy and down-sampled measured images. IEEE Trans Image Process 6(12):1646–1658CrossRefGoogle Scholar
  18. 18.
    Patti AJ, Sezan MI, Tekalp AM (1997) Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time. IEEE Trans Image Process 6(8):1064–1076CrossRefGoogle Scholar
  19. 19.
    Bishop CM, Blake A, Marthi B (2003) Super-resolution enhancement of video. In: Bishop CM, Frey B (eds) Proceedings artificial intelligence and statistics. Society for Artificial Intelligence and Statistics, 2003Google Scholar
  20. 20.
    Dedeoglu G, Kanade T, August J (2004) High-zoom video hallucination by exploiting spatio-temporal regularities. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition (CVPR 04), June, 2004Google Scholar
  21. 21.
    ITU-T (2000) Recommendation ITU-R BT500-10. Methodology for the subjective assessment of the quality of the television pictures, March 2000Google Scholar
  22. 22.
    ITU-T (1999) Recommendation ITU-R P910. Subjective video quality assessment methods for multimedia applications, September 1999Google Scholar
  23. 23.
    VQEG, Video Quality recommendations, VQEG testplans, ftp://vqeg.its.bldrdoc.gov
  24. 24.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Laboratory XLIM-SIC, UMR CNRS 7252University of PoitiersPoitiersFrance

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