TY - GEN

T1 - Learning Defects in Old Movies from Manually Assisted Restoration

AU - Renaudeau, Arthur

AU - Seng, Travis

AU - Carlier, Axel

AU - Pierre, Fabien

AU - Lauze, Francois Bernard

AU - Aujol, Jean François

AU - Durou, Jean-Denis

PY - 2021

Y1 - 2021

N2 - We propose to detect defects in old movies, as the first step of a larger framework of old movies restoration by inpainting techniques. The specificity of our work is to learn a film restorer's expertise from a pair of sequences, composed of a movie with defects, and the same movie which was semiautomatically restored with the help of a specialized software. In order to detect those defects with minimal human interaction and further reduce the time spent for a restoration, we feed a U-Net with consecutive defective frames as input to detect the unexpected variations of pixel intensity over space and time. Since the output of the network is a mask of defect location, we first have to create the dataset of mask frames on the basis of restored frames from the software used by the film restorer, instead of classical synthetic ground truth, which is not available. These masks are estimated by computing the absolute difference between restored frames and defectuous frames, combined with thresholding and morphological closing. Our network succeeds in automatically detecting real defects with more precision than the manual selection with an all-encompassing shape, including some the expert restorer could have missed for lack of time.

AB - We propose to detect defects in old movies, as the first step of a larger framework of old movies restoration by inpainting techniques. The specificity of our work is to learn a film restorer's expertise from a pair of sequences, composed of a movie with defects, and the same movie which was semiautomatically restored with the help of a specialized software. In order to detect those defects with minimal human interaction and further reduce the time spent for a restoration, we feed a U-Net with consecutive defective frames as input to detect the unexpected variations of pixel intensity over space and time. Since the output of the network is a mask of defect location, we first have to create the dataset of mask frames on the basis of restored frames from the software used by the film restorer, instead of classical synthetic ground truth, which is not available. These masks are estimated by computing the absolute difference between restored frames and defectuous frames, combined with thresholding and morphological closing. Our network succeeds in automatically detecting real defects with more precision than the manual selection with an all-encompassing shape, including some the expert restorer could have missed for lack of time.

U2 - 10.1109/ICPR48806.2021.9413196

DO - 10.1109/ICPR48806.2021.9413196

M3 - Article in proceedings

SN - 978-1-7281-8809-6

SP - 5254

EP - 5261

BT - 2020 25th International Conference on Pattern Recognition (ICPR)

PB - IEEE

T2 - 25th International Conference on Pattern Recognition, ICPR 2020

Y2 - 10 January 2021 through 15 January 2021

ER -