Joint detection, interpolation, motion and parameter estimation for image sequences with missing data

  • Anil C. Kokaram
  • Simon J. Godsill
Special Session on European Projects
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)


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.


Image Sequence Motion Vector Gibbs Sampler Motion Field Degraded Image 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Anil C. Kokaram
    • 1
  • Simon J. Godsill
    • 1
  1. 1.Signal Processing and Communications Group, Engineering Dept.University of CambridgeCambridgeEngland

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