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Proceedings Paper

Bayesian image superresolution for hyperspectral image reconstruction
Author(s): Yusuke Murayama; Ari Ide-Ektessabi
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Paper Abstract

This study presents a novel method which applies superresolution to hyperspectral image reconstruction in order to achieve a more efficient spectral imaging method. Theories of spectral reflectance estimation, such as Wiener estimation, have reduced the time and problems faced in spectral imaging. Recently Wiener estimation has been extended to increase not only the spectral resolution but also the spatial resolution of a hyperspectral image by combining the methods for image deblurring. However, there is a demand for more efficient spectral imaging techniques. This study extended the Wiener estimation further to achieve superresolution beyond simple deblurring because superresolution has more advantages: the possibility of getting higher spatial resolution, and the automatic registration of multispectral images. Maximization of the marginal likelihood function is employed in this method to reconstruct the high resolution hyperspectral image on the basis of Bayesian image superresolution. The obvious effect of superresolution was validated through an experiment using acquired multispectral images of a Japanese traditional painting.

Paper Details

Date Published: 10 February 2012
PDF: 8 pages
Proc. SPIE 8296, Computational Imaging X, 829614 (10 February 2012); doi: 10.1117/12.908044
Show Author Affiliations
Yusuke Murayama, Kyoto Univ. (Japan)
Ari Ide-Ektessabi, Kyoto Univ. (Japan)


Published in SPIE Proceedings Vol. 8296:
Computational Imaging X
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)

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