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Deep learning approach for artefacts correction on photographic films

Authors
  • Strubel, David
  • Marc, Blanchon
  • Fofi, David
Publication Date
May 15, 2019
Identifiers
DOI: 10.1117/12.2521421
OAI: oai:HAL:hal-02369128v1
Source
HAL-SHS
Keywords
Language
English
License
Unknown
External links

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.

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