Abstract
In this paper, we describe an automatic method for detecting and repairing non-repetitive damages in image sequences, caused by dust, fibers or local defects of the film emulsion. The method is a three frame window scheme based on the calculation of the optical flow (OF) relating adjacent frames and the first and the last frames of the sequence. The OF validity is checked in order to detect non-repetitive damage, and is later repaired using filtering and smooth blending of the damaged zones. The method works correctly for the set of tested image sequences providing perfect visual repairs of the damaged zones.
This work has been partially supported by The Spanish Ministry of Science and Innovation under contract TIN2007-60625 and by FEDER funds.
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Van Roosmalen, P.: Restoration of Archived Film and Video, Delft University of Technology, Ph.D. Thesis (1999)
Joyeux, L., Boukir, S., Besserer, B., Buisson, O.: Reconstruction of Degraded Image Sequences. Application to Film Restoration. Image and Vision Computing 19(8), 503–516 (2001)
Harvey, N., Marshall, S.: Application of Non-Linear Image Processing: Digital Video Restoration. In: Int. Conf. on Image Processing, vol. 1, pp. 731–734 (1997)
Boulanger, J., Kervrann, C., Bouthemy, P.: Space-Time Adaptation for Patch-Based Image Sequence Restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 1096–1102 (2007)
Joyeux, L., Buisson, O., Besserer, B., Boukir, S.: Detection and Removal of Line Scratches in Motion Picture Films. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 548–553 (1999)
Patwardhan, K., Sapiro, G., Bertalmío, M.: Video Inpainting Under Constrained Camera Motion. IEEE Transactions on Image Processing 16(2), 545–553 (2007)
Álvarez, L., Weickert, J., Sánchez, J.: Reliable Estimation of Dense Optical Flow Fields with Large Displacements. Int. Journal of Computer Vision 39(1), 41–56 (2000)
Beauchemin, S., Barron, J.: The Computation of Optical Flow. ACM Computing Surveys 27(3), 433–467 (1995)
McCane, B., Novins, K., Crannitch, D., Galvin, B.: On Benchmarking Optical Flow. Computer Vision and Image Understanding 84(1), 126–143 (2001)
Horn, B., Schunk, B.: Determining Optical Flow, AI Memo 572. Massachusetts Institute of Technology (1980)
Papenberg, N., Bruhn, A., Brox, T., Didas, S., Weickert, J.: Highly Accurate Optic Flow Computation with Theoretically Justified Warping. Int. Journal of Computer Vision 67(2), 141–158 (2006)
Minelly, S., Curley, A., Giaccone, P., Jones, G.: Reducing Chromatic Grain Noise in Film Sequences. In: IEEE Colloquium on Non-Linear Signal and Image Processing (Ref. No. 1998/284), vol. 5, pp. 1–5 (1998)
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© 2009 Springer-Verlag Berlin Heidelberg
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Dudek, R., Cuenca, C., Quintana, F. (2009). An Automatic Optical Flow Based Method for the Detection and Restoration of Non-repetitive Damaged Zones in Image Sequences. In: Badioze Zaman, H., Robinson, P., Petrou, M., Olivier, P., Schröder, H., Shih, T.K. (eds) Visual Informatics: Bridging Research and Practice. IVIC 2009. Lecture Notes in Computer Science, vol 5857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05036-7_76
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DOI: https://doi.org/10.1007/978-3-642-05036-7_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05035-0
Online ISBN: 978-3-642-05036-7
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