Reconstruction of severely degraded image sequences

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


The AURORA project (AUtomated Restoration of ORiginal Film Archives) is an E.U. funded ACTS project which began in September of 1996. The partners include three broadcasters and holders of archives, The Insitut National L'Audiovisuel (INA), The British Broadcasting Corporation, Radiotelevisao Portuguesa, two industrial companies, Snell and Wilcox, Societe Generale de Teleinformatic, and the signal processing groups of three academic institutions the Digital Media Institute, (Tampere, Finland) Delft University (The Netherlands) and Cambridge University Engineering Dept. (U.K). The project, coordinated by INA, has the sole purpose of designing new tools for video restoration/enhancement. This goal is motivated by the lack of a complete set of advanced manipulation tools which would otherwise allow the more complete exploitation of the archive holdings of many of the larger broadcasters. Furthermore, with the oncoming rise in Digital Video broadcasting a higher demand on quality and quantity of archive material is perceived; hence the requirement for real time restoration devices is set to become more exacting. The project therefore considers the usual cornerstones of video restoration : noise reduction, missing data detection and reconstruction, reduction of image unsteadiness; as well as the associated software and hardware implementation issues. This paper concentrates on new developments at Cambridge University with respect to missing data reconstruction using probabilistic formulations.


Motion Vector Gibbs Sampler Motion Field Markov Random Field Modelling Motion Smoothness 
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.


  1. 1.
    A. Kokaram, R. Morris, W. Fitzgerald, and P. Rayner. Detection of missing data in image sequences.. IEEE Image Processing, pages 1496–1508, Nov. 1995.Google Scholar
  2. 2.
    A. Kokaram, R. Morris, W. Fitzgerald, and P. Rayner. Interpolation of missing data in image sequences.. IEEE Image Processing, pages 1509–1519, Nov. 1995.Google Scholar
  3. 3.
    S. Geman and D. Geman. Stochastic relaxation, gibbs distributions and the bayesian restoration of images. IEEE PAMI, 6:721–741, 1984.Google Scholar
  4. 4.
    J.J. O Ruanaidh and W. J. Fitzgerald. Numerical Bayesian Methods Applied to Signal Processing. Springer Verlag, Springer Series in Statistics and Computing, 1996.Google Scholar
  5. 5.
    S. Z. Li. Markov Random Field Modelling in Computer Vision. Springer Verlag, 1995.Google Scholar
  6. 6.
    R. D. Morris. Image Sequence Restoration using Gibbs Distributions. PhD thesis, Cambridge University, England, 1995.Google Scholar
  7. 7.
    A. Kokaram and S. Godsill. A system for reconstruction of missing data in image sequences using sampled 3D AR models and MRF motion priors. In European Conference on Computer Vision 1996, pages 613–624. Springer Verlag, April 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

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

Personalised recommendations