Deep learning for paint loss detection with a multiscale, translation invariant network


We explore the potential of deep learning in digital painting analysis to facilitate condition reporting and to support restoration treatments. We address the problem of paint loss detection and develop a multiscale deep learning system with dilated convolutions that enables a large receptive field with limited training parameters to avoid overtraining. Our model handles efficiently multimodal data that are typically acquired in art investigation. As a case study we use multimodal data of the Ghent Altarpiece. Our results indicate huge potential of the proposed approach in terms of accuracy and also its fast execution, which allows interactivity and continuous learning.

Proceedings of the 11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019)
Laurens Meeus
Doctoral researcher

My research interests include image processing, deep learning and art investigation.