Open AccessDigital cleaning and “dirt” layer visualization of an oil painting
Author(s) -
Cherry May T. Palomero,
Maricor Soriano
Publication year - 2011
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.19.021011
Subject(s) - dirt , rgb color model , painting , oil painting , artificial intelligence , computer science , computer graphics (images) , computer vision , filter (signal processing) , digital image , optics , transformation (genetics) , point (geometry) , pixel , visualization , image processing , image (mathematics) , art , visual arts , mathematics , physics , chemistry , geography , cartography , biochemistry , geometry , gene
We demonstrate a new digital cleaning technique which uses a neural network that is trained to learn the transformation from dirty to clean segments of a painting image. The inputs and outputs of the network are pixels belonging to dirty and clean segments found in Fernando Amorsolo's Malacañang by the River. After digital cleaning we visualize the painting's discoloration by assuming it to be a transmission filter superimposed on the clean painting. Using an RGB color-to-spectrum transformation to obtain the point-per-point spectra of the clean and dirty painting images, we calculate this "dirt" filter and render it for the whole image.