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An Introduction to Super-Resolution Text

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Digital Document Processing

Part of the book series: Advances in Pattern Recognition ((ACVPR))

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References

  1. Park, S.C., Park, M.K., and Moon, G.K. (2003). Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine, 3, pp. 21-36.

    Article  Google Scholar 

  2. Zandifar, A., Duraiswami, R., Chahine, A., and Davis, L.S. (2002). A video-based interface to textual information for the visually impaired. Proceedings of ICMI, pp. 325-330.

    Google Scholar 

  3. Donaldson, K. and Myers, G.K. (2005). Bayesian super-resolution of text in video with a text-specific bimodal prior. Int. J. Document Analysis and Recognition. Vol. 7, No. 2-3, pp. 159-167.

    Article  Google Scholar 

  4. Shimizu, M., Yano, T., and Okutomi, M. (2004). Super-resolution under image deformation. IEEE Proceedings of the International Conference on Pattern Recognition, pp. 586-589.

    Google Scholar 

  5. Liang, J., Doermann, D., and Li, H. (2005). Camera-based analysis of text and documents: a survey. Int. J. Document Analysis and Recognition. Vol. 7, No. 2-3, pp. 84-104.

    Article  Google Scholar 

  6. Clark, P. and Mirmehdi, M. (2002). Recognising text in real scenes. International Journal of Document Analysis and Recognition, 4, pp. 243-257.

    Article  Google Scholar 

  7. Mirmehdi, M., Clark, P., and Lam, J. (2003). A non-contact method of capturing low-resolution text for OCR. Pattern Analysis and Applications, 1, pp. 12-22.

    Article  MathSciNet  Google Scholar 

  8. Myers, G.K., Bolles, R.C., Luong, Q.-T., Herson, J.A., and Aradhye, H.B. (2005). Rectification and recognition of text in 3-D scenes. Int. J. Document Analysis and Recognition. Vol. 7, No. 2-3, pp. 147-158.

    Article  Google Scholar 

  9. Farsiu, S., Robinson, D., Elad, M., and Milanfar, P. (2004). Advances and challenges in super-resolution. International Journal of Imaging Systems and Technology, 2, pp. 47-57.

    Article  Google Scholar 

  10. Kuglin, C. and Hines, D.(1975). The phase correlation image alignment method. Proceedings of International Conference Cybernetics Society, 163-165.

    Google Scholar 

  11. Li, H. and Doermann, D. (1999). Text enhancement in digital video using multiple frame integration. Proceedings of the ACM International Conference on Multimedia, pp. 19-22.

    Google Scholar 

  12. Fletcher, L. and Zelinsky, A.(2004). Super-resolving signs for classification. Proceedings of Australasian Conference on Robotics and Automation,Australia.

    Google Scholar 

  13. Capel, D.P. (2001). Image mosaicing and super-resolution. PhD thesis.

    Google Scholar 

  14. Harris, C.J. and Stephens, M. (1988). A combined corner and edge detector. Proceedings of the Fourth Alvey Vision Conference, UK, pp. 147-151.

    Google Scholar 

  15. Fischler, M. and Bolles, R. (1981). Random sample consensus: a paradigm for model fitting applications to image analysis and automated cartography. Communications of ACM, pp. 381-395.

    Google Scholar 

  16. Chiang, M.-C. and Boult, T.E. (1997). Local blur estimation and super-resolution. Proceedings of IEEE Computer Vision and Pattern Recognition, pp. 821-826.

    Google Scholar 

  17. Tsai, R.Y. and Huang, T.S. (1984). Multiple frame image restoration and registration. Advances in Computer Vision and Image Processing, pp. 317-339.

    Google Scholar 

  18. Tekalp, A. (1995). Digital Video Processing. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  19. Kim, S., Bose, N., and Valenzuela, H. (1990). Recursive reconstruction of high resolution image from noisy undersampled multiframes. IEEE Transactions on Acoustics, Speech and Signal Processing, 6, pp. 1013-1027.

    Article  Google Scholar 

  20. Irani, M. and Peleg, S. (1991). Improving resolution by image registration. CVGIP: Graphical Models and Image Processing, pp. 231-239.

    Google Scholar 

  21. Capel, D. and Zisserman, A. (2000). Super-resolution enhancement of text image sequences. Proceedings of ICPR, pp. 1600-1605.

    Google Scholar 

  22. Stark, H. and Oskoui, P. (1989). High resolution image recovery from image-plane arrays, using convex projections. Journal of Optical Society of America, pp. 1715-1726.

    Google Scholar 

  23. Patti, A.J., Sezan, M.I., and Tekalp, A.M. (1997). Superresolution video re-construction with arbitrary sampling lattices and non-zero aperture time. IEEE Transactions on Image Processing, 8, pp. 1064-1076.

    Article  Google Scholar 

  24. Elad, M. and Feuer, A. (1997). Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images. IEEE Transactions on Image Processing, 12, pp. 1646-1658.

    Article  Google Scholar 

  25. Kilmer, M.E and O'Leary, D.P. (2001). Choosing reguralization parameters in iterative methods for Iillposed problems. SIAM Journal on Matrix Analysis Applications, 4, 1204-1221.

    Article  MathSciNet  Google Scholar 

  26. Farsiu, S., Robinson, M.D., Elad, M., and Milanfar, P. (2004). Fast and robust multiframe super-resolution. IEEE Transactions on Image Processing, 10, pp. 1327-1344.

    Article  Google Scholar 

  27. Rav-Acha, A. and Peleg, S. (2005). Two motion-blurred images are better than one. Pattern Recognition Letters, pp. 311-317.

    Google Scholar 

  28. Chan, T.F. and Wong, C.K. (1998). Total variation blind deconvolution. IEEE Transactions on Image Processing, 3, pp. 370-375.

    Article  Google Scholar 

  29. Zhao, W., Sawhney, H., Hansen, M., and Samarasekera, S. (2002). Super-fusion: a super-resolution method based on fusion. Proceedings of ICPR, pp. 269-272.

    Google Scholar 

  30. Keren, D., Peleg, S., and Brada, R. (1988). Image sequence enhancement using sub-pixel displacements. Proceedings on CVPR, pp. 742-746.

    Google Scholar 

  31. Mitra, S.K. and Sicuranza, G.L. (2001). Nonlinear Image Processing. New York: Academic Press.

    Google Scholar 

  32. Ramponi, G. (1996). The rational filter for image smoothing. IEEE Signal Processing Letters, 3, pp. 63-65.

    Article  Google Scholar 

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Mancas-Thillou, C., Mirmehdi, M. (2007). An Introduction to Super-Resolution Text. In: Chaudhuri, B.B. (eds) Digital Document Processing. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84628-726-8_14

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  • DOI: https://doi.org/10.1007/978-1-84628-726-8_14

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-501-1

  • Online ISBN: 978-1-84628-726-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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