Abstract
This paper presents methods for detection and reconstruction of ‘missing’ data in image sequences which can be modelled using 3-dimensional autoregressive (3D-AR) models. The interpolation of missing data is important in many areas of image processing, including the restoration of degraded motion pictures, reconstruction of drop-outs in digital video and automatic ‘re-touching’ of old photographs. Here a probabilistic Bayesian framework is adopted and an adaptation of the Gibbs Sampler [1, 2] is used for optimization of the resulting non-linear objective functions. The method assumes no prior knowledge of the motion field or 3D-AR model parameters as these are estimated jointly with the missing image pixels. Incorporating a degradation model into the framework allows detection to proceed jointly with interpolation.
Work funded by European Contract AC072, Automated Restoration of Original Film Archives (AURORA)
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© 1997 Springer-Verlag Berlin Heidelberg
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Kokaram, A.C., Godsill, S.J. (1997). Joint detection, interpolation, motion and parameter estimation for image sequences with missing data. In: Del Bimbo, A. (eds) Image Analysis and Processing. ICIAP 1997. Lecture Notes in Computer Science, vol 1311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63508-4_188
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DOI: https://doi.org/10.1007/3-540-63508-4_188
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