A neural network for digital cleaning of an oil painting
Abstract
We demonstrate that a neural network can be trained to learn the transformation from dirty to clean segments of a painting. The inputs to the network are pixels belonging to dirty paint segments and the desired outputs are pixels from clean segments. We find that the transformation from dirty to clean portions is nonlinear which is contrary to the assumption of most of the previous works on digital cleaning. Finally, we use the neural network to virtually clean a digital image of Fernando Amorsolo’s Malacañang by the River.
Downloads
Published
Issue
Section
License
By submitting their manuscript to the Samahang Pisika ng Pilipinas (SPP) for consideration, the Authors warrant that their work is original, does not infringe on existing copyrights, and is not under active consideration for publication elsewhere.
Upon acceptance of their manuscript, the Authors further agree to grant SPP the non-exclusive, worldwide, and royalty-free rights to record, edit, copy, reproduce, publish, distribute, and use all or part of the manuscript for any purpose, in any media now existing or developed in the future, either individually or as part of a collection.
All other associated economic and moral rights as granted by the Intellectual Property Code of the Philippines are maintained by the Authors.
