Perceptual Loss based Approach for Analogue Film Restoration

Analogue film restoration, both for still photographs and motion picture emulsions, is a slow and laborious manual process. Artifacts such as dust and scratches are random in shape, size, and location; additionally, the overall degree of damage varies between different frames. We address this less popular case of image restoration by training a U-Net model with a modified perceptual loss function. Along with the novel perceptual loss function used for training, we propose a more rigorous quantitative model evaluation approach which measures the overall degree of improvement in perceptual quality over our test set.