Bayesian super-resolution of text image sequences from low resolution observations

Francisco J. Cortijo, Salvador Villena, Rafael Molina, Aggelos Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

This paper deals with the problem of reconstructing high-resolution text images from an incomplete set of under-sampled, blurred, and noisy images shifted with subpixel displacement. We derive mathematical expressions for the calculation of the maximum a posteriori estimate of the high resolution image and the estimation of the parameters involved in the model. The method is tested on real text images and car plates, examining the impact of blurring and the number of available low resolution images on the final estimate.

Original languageEnglish (US)
Title of host publicationProceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
PublisherIEEE Computer Society
Pages421-424
Number of pages4
ISBN (Print)0780379462, 9780780379466
DOIs
StatePublished - 2003
Event7th International Symposium on Signal Processing and Its Applications, ISSPA 2003 - Paris, France
Duration: Jul 1 2003Jul 4 2003

Publication series

NameProceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
Volume1

Other

Other7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
CountryFrance
CityParis
Period7/1/037/4/03

ASJC Scopus subject areas

  • Signal Processing

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  • Cite this

    Cortijo, F. J., Villena, S., Molina, R., & Katsaggelos, A. (2003). Bayesian super-resolution of text image sequences from low resolution observations. In Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003 (pp. 421-424). [1224730] (Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/ISSPA.2003.1224730