Joint Detection, Interpolation, Motion and Parameter estimation for Image Sequences with Missing Data
Citation:
Kokaram, A.C. ; Godsill, S.J. ;, Joint Detection, Interpolation, Motion and Parameter estimation for Image Sequences with Missing Data, Proceedings of the International Conference on Image Processing, International Conference on Image Processing, Santa Barbara, CA, 26-29 October, 1997, Magarey, J. Kokaram, A. Kingsbury, N., 2, IEEE Computer Society Press, 1997, 191 - 194Download Item:
Joint Detection, Interpolation, Motion and Parameter estimation for.pdf (Published (publisher's copy) - Peer Reviewed) 672.1Kb
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. 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
Author's Homepage:
http://people.tcd.ie/akokaramDescription:
PUBLISHED
Author: KOKARAM, ANIL CHRISTOPHER
Publisher:
IEEE Computer Society PressType of material:
Conference PaperSeries/Report no:
2;Availability:
Full text availableKeywords:
Electrical Engineering, Bayes methodsLicences: