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Communication dans un congrès

Deep learning approach for artefacts correction on photographic films

David Strubel 1, 2 Blanchon Marc 2 David Fofi 2
1 ImViA - Imagerie et Vision Artificielle [Dijon]
2 VIBOT - Equipe VIBOT - VIsion pour la roBOTique [ImViA EA7535 - ERL CNRS 6000]
CNRS - Centre National de la Recherche Scientifique : ERL 6000, ImViA - Imagerie et Vision Artificielle [Dijon]
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.
Keywords : quality control deep learning photographic film artefact removal
Type de document :
Communication dans un congrès
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-02369128
Contributeur : Imvia - Université de Bourgogne <>
Soumis le : lundi 18 novembre 2019 - 17:54:24
Dernière modification le : mercredi 22 janvier 2020 - 15:44:03

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UNIV-BOURGOGNE | IMVIA | VIBOT | CNRS

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David Strubel, Blanchon Marc, David Fofi. Deep learning approach for artefacts correction on photographic films. Fourteenth International Conference on Quality Control by Artificial Vision, May 2019, Mulhouse, France. pp.35, ⟨10.1117/12.2521421⟩. ⟨hal-02369128⟩

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