Abstract

We propose a novel restoration method for defects and miss- ing regions in video sequences, particularly in application to archive film restoration. Our statistical framework is based on random walks to ex- amine the spatiotemporal path of a degraded pixel, and uses texture features in addition to intensity and motion information traditionally used in previous restoration works. The degraded pixels within a frame are restored in a multiscale framework by updating their features (inten- sity, motion and texture) at each level with reference to the attributes of normal pixels and other defective pixels in the previous scale as long as they fall within the defective pixel’s random walk-based spatiotem- poral neighbourhood. The proposed algorithm is compared against two state-of-the-art methods to demonstrate improved accuracy in restoring synthetic and real degraded image sequences.