Augmenting Polarized Light Microscopy with Computational Imaging and Deep Learning for Cultural Heritage

FAIN: PR-284405-22

Northwestern University (Evanston, IL 60208-0001)
Marc Sebastian Walton (Project Director: May 2021 to March 2022)
Aggelos K. Katsaggelos (Project Director: March 2022 to present)
Aggelos K. Katsaggelos (Co Project Director: February 2022 to March 2022)
Oliver Strides Cossairt (Co Project Director: February 2022 to present)
Florian Willomitzer (Co Project Director: February 2022 to present)

The development of image-based, quantitative protocols for Polarized Light Microscopy (PLM) using hardware and deep learning algorithms to generate image data for pigment identification and diagnosis of patterns of deterioration.

The project proposed here builds on this significant infrastructure and know-how within the conservation profession on PLM use. Focusing on the extensive archive of pigment dispersion slides at the Art Institute of Chicago1 and the Forbes collection at Harvard Art Museums as source materials, this proposal aims to maximize the amount of information extracted from PLM through recent advances in sensor hardware combined with computational imaging and deep learning. In short, we will be modernizing PLM by "harnessing the data revolution"to provide cutting-edge resources for conservators to make pigment identifications and to diagnose patterns of deterioration. As a core part of our dissemination, we will be making both the data collected as well as software pipelines open source for use by anyone and accessible through the Center of Scientific Studies in the Arts'' (NU-ACCESS) online presence.