Learning Defects in Old Movies from Manually Assisted Restoration

Abstract

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.

Research on Scratch Detection of Old Movies

“…With the emergence of convolution neural networks, some scholars have used a neural network to detect and repair scratches. For example, references [11,12] use a U-net to detect scratch damage in old movies, but the actual test results can only detect the parts with simple background information in the main scratches. Iizuka et al Put forward DeepRemaster [13] , which uses time convolution to restore old movie damage and repair video artifacts, but has a poor perception of scratch damage.…”