Back to articles
Articles
Volume: 5 | Article ID: art00030
Image
A New Proposal for the Accurate Recovery of Spectral Reflectances of Imaged Objects without Prior Knowledge
Noriyuki Shimano
Mikiya Hironaga
  DOI :  10.2352/issn.2168-3204.2008.5.1.art00030  Published Online :  January 2008
Abstract

Recovery of spectral reflectances of imaged objects is important to reproduce color images under arbitrary illuminations. In this paper, a new method is proposed to recover spectral reflectances accurately through the use of image data without prior knowledge of the imaged objects. It is shown that the Wiener estimation which uses the noise variance estimated by training samples and the features of the recovered spectral reflectances is effective to improve the recovery performance.

Journal Title : Archiving Conference
Publisher Name : Society of Imaging Science and Technology
Publisher Location : 7003 Kilworth Lane, Springfield, VA 22151, USA
Subject Areas :
Views 5
Downloads 1
 DOI 10.2352/issn.2168-3204.2008.5.1.art00030
 articleview.views 5
 articleview.downloads 1
  Cite this article 

Noriyuki Shimano, Mikiya Hironaga, "A New Proposal for the Accurate Recovery of Spectral Reflectances of Imaged Objects without Prior Knowledge"  in Proc. IS&T Archiving 2008,  2008,  pp 155 - 158,  https://doi.org/10.2352/issn.2168-3204.2008.5.1.art00030

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2008
72010361
Archiving Conference
archiving
2161-8798
Society of Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151, USA
2161-8798(20080101)2008:1L.155;1-
ac_v2008n1/splitsection30.xml
/ist/ac/2008/00002008/00000001/art00030
Articles
A New Proposal for the Accurate Recovery of Spectral Reflectances of Imaged Objects without Prior Knowledge
ShimanoNoriyuki
HironagaMikiya
01012008
2008
1
155
158
2008
Recovery of spectral reflectances of imaged objects is important to reproduce color images under arbitrary illuminations. In this paper, a new method is proposed to recover spectral reflectances accurately through the use of image data without prior knowledge of the imaged objects. It is shown that the Wiener estimation which uses the noise variance estimated by training samples and the features of the recovered spectral reflectances is effective to improve the recovery performance.