Abstract
This paper deals with the problem of reconstructing a high-resolution image from an incomplete set of undersampled, 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. We also examine the role played by the prior model when this incomplete set of low resolution images is used. The performance of the method is tested experimentally.
This work has been partially supported by the “Comisión Nacional de Ciencia y Tecnología” under contract TIC2000-1275.
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Mateos, J., Vega, M., Molina, R., Katsaggelos, A.K. (2003). Bayesian Image Estimation from an Incomplete Set of Blurred, Undersampled Low Resolution Images. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_63
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DOI: https://doi.org/10.1007/978-3-540-44871-6_63
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