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Archive Film Defect Detection and Removal: An Automatic Restoration Framework

Xiaosong Wang, Majid Mirmehdi, Archive Film Defect Detection and Removal: An Automatic Restoration Framework. IEEE Transactions on Image Processing, 21(8), pp. 3757–3769. August 2012. No electronic version available. External information


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 combing temporal and spatial information across a number of frames. An HMM 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 labelled (degraded) pixels 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 state-of-the-art methods to demonstrate improved accuracy in both detection and restoration using synthetic and real degraded image sequences.

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