Abstract
The constrained optimization method is employed to calculate the colorant values of the multispectral images. Because the spectral separation from the 31-dimensional spectral reflectance to low dimensional colorant values is very complex, an inverse process based on spectral Neugebauer model and constrained optimization method is performed. Firstly, the spectral Neugebauer model is applied to predict the colorants’ spectral reflectance values, and it is modified by using the Yule-Nielsenn-value and the effective area coverages. Then, the spectral reflectance root mean square (RRMS) error is established as the objective function for the optimization method, while the colorant values are constrained to 0~1. At last, when the nonlinear constraints and related parameters are set appropriately, the colorant values are accurately calculated for the multispectral images corresponding to the minimum RRMS errors. In the experiment, the colorant errors of the cyan, magenta and yellow inks are all below 2.5% and the average spectral error is below 5%, which indicate that the precision of the spectral separation method in this paper is acceptable.
Highlights
The objective of color reproduction is to obtain the same visual perception of the original images, and it is mainly implemented based on the metameric reproduction principle [1, 2]
When a primary ink is printed on the paper, if the spectral reflectance of the solid and substrate are represented as Rλ,t and Rλ,s, respectively, and the patch’s dot area is given, its reflectance values Rλ can be predicted by using Murray-Davies model as follows: Rλ = atRλ,t + (1 − at) Rλ,s, (1)
The spectral separation algorithm is significant for the multispectral image printing devices, which can calculate the accurate colorant values
Summary
The objective of color reproduction is to obtain the same visual perception of the original images, and it is mainly implemented based on the metameric reproduction principle [1, 2]. In many high-accuracy reproduction areas, the originals are represented as multispectral images, not the common RGB/CMYK images [3, 4]. As most of the multispectral image pixels are 31-dimensional, it is difficult to calculate the colorant values from the spectral data straightly. Most of the spectral separation processes are based on the spectral predication models and iteration methods. The spectral predication models can be used to calculate different ink combinations’ spectral values. The iteration of the separation process will stop when the image pixel’s spectral matches with the predicated spectral. Because the spectral Neugebauer model uses less sample colors and often generates acceptable accuracy, it is widely applied to the spectral separation process. Journal of Spectroscopy the multispectral image’s spectral separation accuracy for CMYK printers
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