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Please use this identifier to cite or link to this item: http://hdl.handle.net/2262/37106

Title: Causality considerations for missing data reconstruction in image sequences
Author: KOKARAM, ANIL CHRISTOPHER
Author's Homepage: http://people.tcd.ie/akokaram
Keywords: autoregressive processes
image reconstruction
image sequences
image texture
prediction theory
Issue Date: 1997
Publisher: IEEE
Citation: Wooi-Boon Goh; Kokaram, A.C.; Man-Nang Chong; Rayner, P.J.W, ‘Causality considerations for missing data reconstruction in image sequences’ in Proceedings of the International Conference on Information, Communications and Signal Processing, (ICICS 1997), International Conference on Information, Communications and Signal Processing, (ICICS 1997), 9-12 Sept, eds. Wooi-Boon Goh; Kokaram, A.C.; Man-Nang Chong; Rayner, P.J.W, 3, IEEE, 1997, pp 1575 - 1579
Series/Report no.: 3
Abstract: The 3D autoregressive (AR) model with a non-causal support region has been successfully employed in the reconstruction of texture and missing regions in image sequences. This paper discusses the causality considerations when selecting the reconstruction model. When a distorted area to be reconstructed is large, a substantial computational load reduction can be obtained by implementing a predictor with a purely causal AR support. A novel reconstruction scheme which employs a selective causal/anti-causal (S-C/AC) AR model is presented. Experimental results suggest that the S-C/AC scheme produces a good trade-off between computational and reconstruction performance
Description: PUBLISHED
URI: http://dx.doi.org/10.1109/ICICS.1997.652259
http://hdl.handle.net/2262/37106
Appears in Collections:Electronic & Electrical Eng (Scholarly Publications)

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