Deep learning approach for artefacts correction on photographic films

David Strubel; Marc Blanchon; Fofi David

doi:10.1117/12.2521421

16 July 2019 Deep learning approach for artefacts correction on photographic films

David Strubel, Marc Blanchon, Fofi David

Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 111720M (2019) https://doi.org/10.1117/12.2521421
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France

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.

Citation Download Citation

David Strubel, Marc Blanchon, and Fofi David "Deep learning approach for artefacts correction on photographic films", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111720M (16 July 2019); https://doi.org/10.1117/12.2521421

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