A system for reconstruction of missing data in image sequences using sampled 3D AR models and MRF motion priors

  • Anil C. Kokaram
  • Simon J. Godsill
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1065)


This paper presents a new technique for interpolating missing data in image sequences. A 3D autoregressive (AR) model is employed and a sampling based interpolator is developed in which reconstructed data is generated as a typical realization from the underlying AR process. rather than e.g. least squares (LS). In this way a perceptually improved result is achieved. A hierarchical gradient-based motion estimator, robust in regions of corrupted data, employing a Markov random field (MRF) motion prior is also presented for the estimation of motion before interpolation.


Image Sequence Motion Vector Motion Estimation Markov Random Field Impulsive Noise 
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 1996

Authors and Affiliations

  • Anil C. Kokaram
    • 1
  • Simon J. Godsill
    • 1
  1. 1.Signal Processing and Communications GroupCambridge University Engineering Dept.CambridgeEngland

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