Assessing the capacity of two-flux models to predict the spectral properties of layered materials - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Assessing the capacity of two-flux models to predict the spectral properties of layered materials

Résumé

A classical way of coloring a surface in order to create a still image is the application of a colored coating. The more recent digital printing systems enable depositing thick coatings or successive ink layers. The color rendering of the surface depends on the optical properties of the coated materials (optical index, spectral scattering and absorption coefficients) and their thickness. In order to predict its spectral reflectance as a function of these parameters, the so-called two-flux approach is to be tested in first since the model is simple and relies on analytical equations. It has a good chance to provide accurate predictions for coatings made of solid layers of strongly scattering or nonscattering media, or even complex stratified coatings obtained by stacking nonsymmetrical components such as printed films. The generalized Kubelka-Munk model summarized in this paper enables treating all these configurations with a unified mathematical formalism. But it has limitations and may provide poor color predictions for certain types of layered materials. We therefore propose a simple method based on parameters of the model to check the precision of the two-flux model for a given type of coating.
Fichier principal
Vignette du fichier
2016EI_Hebert_AlbedoStratifiedMedia.pdf (585.57 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01277062 , version 1 (22-02-2016)

Identifiants

  • HAL Id : hal-01277062 , version 1

Citer

Mathieu Hébert, Serge Mazauric, Lionel Simonot. Assessing the capacity of two-flux models to predict the spectral properties of layered materials. Electronic Imaging, Feb 2016, San Francisco, United States. ⟨hal-01277062⟩
102 Consultations
471 Téléchargements

Partager

Gmail Facebook X LinkedIn More