Related Experiment Video
Updated: Jul 19, 2026

04:32
Quantitative Characterization of Liquid Photosensitive Bioink Properties for Continuous Digital Light Processing Based Printing
Published on: April 14, 2023
Reflectance and transmittance model for recto-verso halftone prints
Mathieu Hébert1, Roger David Hersch
1Ecole Polytechnique Fédérale de Lausanne, Switzerland. mathieu.herbert@epfl.ch
Summary
We developed a new spectral prediction model for recto-verso halftone prints, enhancing reflectance and transmittance predictions. This model unifies existing methods and offers new ways to characterize printed substrates.
Area of Science:
- Color Science
- Optical Physics
- Materials Science
Background:
- Accurate spectral prediction of halftone prints is crucial for color reproduction.
- Existing models like Williams-Clapper and Kubelka-Munk have limitations for recto-verso prints.
- Recto-verso prints involve complex light interactions with diffusing substrates and inked interfaces.
Purpose of the Study:
- To propose a novel spectral prediction model for recto-verso halftone prints.
- To unify and extend existing spectral prediction models (Williams-Clapper, Clapper-Yule, Kubelka-Munk).
- To provide a framework for characterizing printed diffuse substrates.
Main Methods:
- Modeled recto-verso halftone prints as diffusing substrates with two inked interfaces.
- Accounted for light attenuation at interfaces and internal reflections within the substrate.
- Extended the Kubelka-Munk model for compatibility with the proposed recto-verso model.
Main Results:
- The proposed model encompasses classical Williams-Clapper and Clapper-Yule models as special cases.
- Demonstrated compatibility between the extended Kubelka-Munk model and the recto-verso model.
- Showed that internal reflectance/transmittance can be related to Kubelka-Munk parameters for homogeneous substrates.
Conclusions:
- The proposed spectral prediction model offers improved accuracy for recto-verso halftone prints.
- The model provides a unified approach, integrating and extending existing prediction methods.
- This work opens new avenues for spectral prediction and substrate property characterization.

