Segmentation-Assisted Dirt Detection for the Restoration of Archived Films

A novel segmentation-assisted method for film dirt detection is proposed. Since dirt manifests as a cluster of pixels whose intensity differs from that of its neighbourhood, we employ segmentation and assume that each small region as a dirt candidate. The assumption is validated by considering raw (non-motion compensated) differences between the current frame and each of the previous and next frames which provides a measure of a confidence. Our experiments show that our method compares favourably with standard spatial, temporal and multistage median filtering approaches and provides efficient and robust detection even for fast moving sequences.