Blotch and scratch removal in archived film using a semi-transparent corruption model and a ground-truth generation technique

Abstract

This paper has two main contributions. The first is a Bayesian framework for removing two common types of degradations on video known as blotches and line scratches. Most removal techniques assume complete obliteration of the original data at the corrupted sites. This often leads to the introduction of restoration artifacts during removal. Our framework is based on modeling corruption as a semi-transparent layer. This model was introduced earlier by Ahmed et al. (ICIP 2009) for the problem of blotch removal. We show much more blotch removal results than the previous work, and we extend the semi-transparent corruption model to the problem of line removal. The second contribution of this paper is an automated technique for ground-truth generation from infrared scans of corruptions. Previous ground-truth generation efforts require manually inpainting the corrupted regions. The restoration results are evaluated by comparing the reconstructed data against the ground-truth estimates. Comparisons with current blotch and line removal techniques show that the proposed corruption removal framework produces better removal and generates less restoration artifacts.