Archive Film Defect Detection and Removal: An Automatic Restoration Framework

In this paper, we present an automatic restoration system targeting on dirt and blotches in digitized archive films. The system is composed of mainly two modules: defect detection and defect removal. In defect detection, we locate the defects by combining temporal and spatial information across a number of frames. A hidden Markov model is trained for normal observation sequences and then applied within a framework to detect defective pixels. The resulting defect maps are refined in a two-stage false alarm elimination process and then passed over to the defect removal procedure. A labeled (degraded) pixel is restored in a multiscale framework by first searching the optimal replacement in its dynamically generated random-walk-based region of candidate pixel-exemplars and then updating all its features (intensity, motion, and texture). Finally, the proposed system is compared against the state-of-the-art methods to demonstrate improved accuracy in both detection and restoration using synthetic and real degraded image sequences.