Application of Bayesian image superresolution to spectral reflectance estimation

Yusuke Murayama; Ari Ide-Ektessabi

doi:10.1117/1.OE.51.11.111713

27 June 2012 Application of Bayesian image superresolution to spectral reflectance estimation

Yusuke Murayama, Ari Ide-Ektessabi

Author Affiliations +

Optical Engineering, Vol. 51, Issue 11, 111713 (June 2012). https://doi.org/10.1117/1.OE.51.11.111713

Abstract

Hyperspectral imaging provides us with high-dimensional, scene-independent color information but faces problems such as long image acquisition time and severe lighting and focusing conditions. To achieve efficient spectral imaging, this study presents an extended method of Bayesian image superresolution. The proposed method increases both the spatial and the wavelength resolution of input images and enables the processing of hyperspectral images to a higher resolution from easily acquired low-spatial-resolution multispectral images. In an experiment using acquired multispectral images of a Japanese traditional painting, visible and near-infrared hyperspectral images were produced, and the obvious effect of superresolution was validated.

Citation Download Citation

Yusuke Murayama and Ari Ide-Ektessabi "Application of Bayesian image superresolution to spectral reflectance estimation," Optical Engineering 51(11), 111713 (27 June 2012). https://doi.org/10.1117/1.OE.51.11.111713

Published: 27 June 2012

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available