autoregressive processes image reconstruction image sequences image texture prediction theory
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
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
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