Back to articles
Articles
Volume: 29 | Article ID: art00027
Image
Spectral band Selection Using a Genetic Algorithm Based Wiener Filter Estimation Method for Reconstruction of Munsell Spectral Data
  DOI :  10.2352/ISSN.2470-1173.2017.18.COLOR-059  Published OnlineJanuary 2017
Abstract

Spectrophotometers are the common devices for reflectance measurements. However, there are some drawbacks associated with these devices. Price, sample size and physical state are the main difficulties in applying them for reflectance measurement. Spectral estimation using a set of camera-filters is the eligibly solution for avoiding these difficulties. Meanwhile band selection of filters are needed to be optimized in order to apply in imaging systems. In the present study, the Genetic algorithm was applied for finding the best set of three to eight filters combinations with specific FWHM. The algorithm tries to minimize the color difference between reconstructed and actual spectral data, assuming a simulation of imaging system. This imaging system is composed of a CMOS sensor, illuminant and 1269 matt Munsell spectral data set as the object. All simulations were done in visible spectrum. The optimized filter selections were modeled on a CMOS sensor in order to spectral reflectance reconstruction. The results showed no significant improvement after selecting a seven filter set although a descending trend in the color difference errors was obtained with increasing the number of filters.

Subject Areas :
Views 130
Downloads 3
 articleview.views 130
 articleview.downloads 3
  Cite this article 

Keivan Ansari, Jean-Baptiste Thomas, Pierre Gouton, "Spectral band Selection Using a Genetic Algorithm Based Wiener Filter Estimation Method for Reconstruction of Munsell Spectral Datain Proc. IS&T Int’l. Symp. on Electronic Imaging: Color Imaging XXII: Displaying, Processing, Hardcopy, and Applications,  2017,  pp 190 - 193,  https://doi.org/10.2352/ISSN.2470-1173.2017.18.COLOR-059

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2017
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology