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Deep learning approach for artefacts correction on photographic films
Author(s): David Strubel; Marc Blanchon; Fofi David
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Paper Abstract

The use of photographic films is not totally obsolete, photographers continue to use this technology for quality in terms of aesthetic rendering. A crucial step with films is the digitization step. During the scanning process, dust, scratch and hair (artefacts) are a real problem and greatly affect the quality of final images. The artefacts correction has become a challenge in order to preserve the quality of these photos. In this article, we present a new method based on deep learning with an encoder-decoder architecture to detect and eliminate artefacts. In addition, a dataset has been created to carry out the experiments.

Paper Details

Date Published: 16 July 2019
PDF: 6 pages
Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111720M (16 July 2019); doi: 10.1117/12.2521421
Show Author Affiliations
David Strubel, Lab. Interdisciplinaire Carnot de Bourgogne (France)
Marc Blanchon, Lab. Interdisciplinaire Carnot de Bourgogne (France)
Fofi David, Lab. Interdisciplinaire Carnot de Bourgogne (France)


Published in SPIE Proceedings Vol. 11172:
Fourteenth International Conference on Quality Control by Artificial Vision
Christophe Cudel; Stéphane Bazeille; Nicolas Verrier, Editor(s)

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