Abstract
Blotches are one of the most common film degradations that must be detected and corrected in the process of film restoration. In this work we will address the problem of blotch detection in the context of digital film restoration. Although there are several methods for blotch detection, in the literature their evaluation is usually subjective. In this work we propose a new method for blotch detection and an objective methodology to evaluate its performance. We show that the proposed method outperforms other existing methods while using this objective metric.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Bouguet: Pyramidal implementation of the lucas kanade feature tracker description of the algorithm. Technical report, Intel Corporation (2002)
Buades, A., Delon, J., Gousseau, Y., Masnou, S.: Adaptive blotches detection for film restoraion. In: ICIP 2010, pp. 3317–3320 (2010)
Desolneux, A., Moisan, L., Morel, J.M.: Gestalt Theory and Image Analysis. Springer, Heidelberg (2007)
Kokaram, A.: Motion Picture Restoration: Digital Algorithms for Artefact Suppression in Degraded Motion Picture Film and Video. Springer, Heidelberg (2001)
Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Image Understanding Workshop, pp. 674–679 (1981)
Pinz, A., Schallauer, P., Haas, W.: Automatic restoration algorithms for 35mm film. J. Computer Vision Research 1(3), 59–85 (1999)
Van Roosmalen, P.M.B.: Restoration of Archvied Film and Video. PhD thesis, Delft University (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pardo, A. (2011). Blotch Detection for Film Restoration. In: San Martin, C., Kim, SW. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2011. Lecture Notes in Computer Science, vol 7042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25085-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-25085-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25084-2
Online ISBN: 978-3-642-25085-9
eBook Packages: Computer ScienceComputer Science (R0)