Deep learning approach for artefacts correction on photographic films
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | Deep learning approach for artefacts correction on photographic films |
| Type de publication | Conference Paper |
| Year of Publication | 2019 |
| Auteurs | David S, Marc B, David F |
| Editor | Cudel C, Bazeille S, Verrier N |
| Conference Name | FOURTEENTH INTERNATIONAL CONFERENCE ON QUALITY CONTROL BY ARTIFICIAL VISION |
| Publisher | Univ Haute Alsace; Mulhouse Alsace Agglomerat; Region Grand Est; IDS GmbH; Fac Sci Mulhouse |
| Conference Location | 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA |
| ISBN Number | 978-1-5106-3054-3 |
| Mots-clés | artefact removal, Deep learning, photographic film, Quality Control |
| Résumé | 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. |
| DOI | 10.1117/12.2521421 |