Framework proposal for high-resolution spectral image acquisition of effect-coatings Valero Benito, Eva María Martínez Domingo, Miguel Ángel Kirchner, Eric van der Lans, Ivo García Fernández, María Eckhard, Timo Micha Huertas Roa, Rafael Spectral imaging Effect coatings High resolution Hyperspectral imaging of effect coated samples can be challenging, mainly because of the large differences in irradiance that stem from the orientation distribution of the metallic flakes contained in the coating, and from the lightness variations from one sample to another. Besides, high spatial resolution is needed to sample the details of the texture (sparkle) typical of these samples. In addition, focus search strategy and image registration are essential to achieve high quality data for further analysis. In this work, we propose and fully validate a capture framework for measuring spectral reflectance of effect-coated samples with high spatial resolution in 45/0 geometry, using an LCTF (Liquid Crystal Tunable Filter) coupled with a monochrome camera. The main features of the proposed framework are an optimized focus search method based on object movement, a very precise alignment for the images captured in different bands (image registration), achieving sub-pixel accuracy, and a dynamic procedure that uses several white reference surfaces in exposure time estimation to cope with very dark or highly reflective samples. The proposed capture device produces spectral reflectance values comparable to a conventional spectroradiometer using the same observation/illumination geometry, with the additional advantage of achieving a spatial resolution more than two times higher than the human visual system. 2024-01-12T08:42:07Z 2024-01-12T08:42:07Z 2019-06-08 journal article Valero, E. M., Martínez, M. A., Kirchner, E., van der Lans, I., García-Fernández, M., Eckhard, T., & Huertas, R. (2019). Framework proposal for high-resolution spectral image acquisition of effect-coatings. Measurement, 145, 379-390. https://hdl.handle.net/10481/86738 https://doi.org/10.1016/j.measurement.2019.05.024 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier