Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26308
Title: Adaptive characterization method for desktop color printers
Authors: Shen, HL
Zheng, ZH
Jin, CC
Du, X
Shao, SJ
Xin, JH 
Issue Date: 2013
Publisher: SPIE-International Society for Optical Engineering
Source: Journal of electronic imaging, 2013, v. 22, no. 2, 023012 How to cite?
Journal: Journal of electronic imaging 
Abstract: Abstract. With the rapid development of multispectral imaging technique, it is desired that the spectral color can be accurately reproduced using desktop color printers. However, due to the specific spectral gamuts determined by printer inks, it is almost impossible to exactly replicate the reflectance spectra in other media. In addition, as ink densities can not be individually controlled, desktop printers can only be regarded as red-green-blue devices, making physical models unfeasible. We propose a locally adaptive method, which consists of both forward and inverse models, for desktop printer characterization. In the forward model, we establish the adaptive transform between control values and reflectance spectrum on individual cellular subsets by using weighted polynomial regression. In the inverse model, we first determine the candidate space of the control values based on global inverse regression and then compute the optimal control values by minimizing the color difference between the actual spectrum and the predicted spectrum via forward transform. Experimental results show that the proposed method can reproduce colors accurately for different media under multiple illuminants.
URI: http://hdl.handle.net/10397/26308
ISSN: 1017-9909
EISSN: 1560-229X
DOI: 10.1117/1.JEI.22.2.023012
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

2
Last Week
0
Last month
0
Citations as of Aug 11, 2017

WEB OF SCIENCETM
Citations

2
Last Week
0
Last month
0
Citations as of Aug 14, 2017

Page view(s)

41
Last Week
1
Last month
Checked on Aug 13, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.