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Deep-doLCE. A deep learning approach for the color reconstruction of digitized lenticular film


Trumpy, Giorgio; D'Aronco, Stefano; Wegner, Jan Dirk; Reuteler, Joakim (2021). Deep-doLCE. A deep learning approach for the color reconstruction of digitized lenticular film. In: Cattaneo, Barbara; Picollo, Marcello; Cherubini, Filippo; Marchiafava, Veronica. Colour photography and film : sharing knowledge of analysis, preservation, conservation, migration of analogue and digital materials – Conference proceedings. Milano: Gruppo del Colore – Associazione Italiana Colore, 82-88.

Abstract

Some of the first home movies in color were shot on 16 mm lenticular film during the 1920s to 1940s. This very special film is embossed with a vertical array of hundreds of tiny cylindrical lenses that allowed to record color scenes on a black&white silver emulsion. The most efficient approach to obtain digital color images from these historical motion pictures is to scan the silver emulsion in high-
resolution and let a software extract the encoded color information. The present work focuses on the localization of the lenticular screen, which is the first and most complicated step of the color reconstruction. A ‘classic’ signal processing method proved to deliver successful results in some cases, but often adverse factors—damaged or warped film, scanning problems—hinder the successful localization of the lenticular screen. Deep-doLCE explores a more advanced and robust method, using a big dataset of digitized lenticular films to train a new deep learning software. The aim is to create an easy-to-use software that revives awareness of the lenticular color processes thus making these precious historical color movies available again to public and securing them for posterity.

Abstract

Some of the first home movies in color were shot on 16 mm lenticular film during the 1920s to 1940s. This very special film is embossed with a vertical array of hundreds of tiny cylindrical lenses that allowed to record color scenes on a black&white silver emulsion. The most efficient approach to obtain digital color images from these historical motion pictures is to scan the silver emulsion in high-
resolution and let a software extract the encoded color information. The present work focuses on the localization of the lenticular screen, which is the first and most complicated step of the color reconstruction. A ‘classic’ signal processing method proved to deliver successful results in some cases, but often adverse factors—damaged or warped film, scanning problems—hinder the successful localization of the lenticular screen. Deep-doLCE explores a more advanced and robust method, using a big dataset of digitized lenticular films to train a new deep learning software. The aim is to create an easy-to-use software that revives awareness of the lenticular color processes thus making these precious historical color movies available again to public and securing them for posterity.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Cinema Studies
08 Research Priority Programs > Digital Society Initiative
Dewey Decimal Classification:700 Arts
900 History
Scopus Subject Areas:Physical Sciences > Electronic, Optical and Magnetic Materials
Physical Sciences > Condensed Matter Physics
Physical Sciences > Computer Science Applications
Physical Sciences > Applied Mathematics
Physical Sciences > Electrical and Electronic Engineering
Uncontrolled Keywords:Lenticular film, color reconstruction, deep learning, film digitization.
Language:English
Date:July 2021
Deposited On:26 Feb 2022 18:54
Last Modified:01 Feb 2024 15:36
Publisher:Gruppo del Colore – Associazione Italiana Colore
ISBN:978-88-99513-14-6
Additional Information:Conference took place in Florence, March 29-30, 2021, organised by Gruppo del Colore – Associazione Italiana Colore, under the patronage of Opificio delle Pietre Dure and Institute of Applied Physics “Nello Carrara” of the Italian Research Council (IFAC-CNR).
OA Status:Green
Official URL:https://www.gruppodelcolore.org/portfolio-articoli/florence-2021/?lang=en
Related URLs:https://www.gruppodelcolore.org/ (Organisation)
Project Information:
  • : FunderH2020
  • : Grant ID812583
  • : Project TitleDevelopment of a Multispectral, Versatile Film Scanner
  • Content: Published Version
  • Language: English