Bayesian image superresolution for hyperspectral image reconstruction

Yusuke Murayama; Ari Ide-Ektessabi

doi:10.1117/12.908044

10 February 2012 Bayesian image superresolution for hyperspectral image reconstruction

Yusuke Murayama, Ari Ide-Ektessabi

Proceedings Volume 8296, Computational Imaging X; 829614 (2012) https://doi.org/10.1117/12.908044
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

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.

Citation Download Citation

Yusuke Murayama and Ari Ide-Ektessabi "Bayesian image superresolution for hyperspectral image reconstruction", Proc. SPIE 8296, Computational Imaging X, 829614 (10 February 2012); https://doi.org/10.1117/12.908044

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