A System for Reconstruction of Missing Data in Image Sequences Using Sampled 3D AR Models and MRF Motion Priors
Abstract
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.
Cite
Text
Kokaram and Godsill. "A System for Reconstruction of Missing Data in Image Sequences Using Sampled 3D AR Models and MRF Motion Priors." European Conference on Computer Vision, 1996. doi:10.1007/3-540-61123-1_175Markdown
[Kokaram and Godsill. "A System for Reconstruction of Missing Data in Image Sequences Using Sampled 3D AR Models and MRF Motion Priors." European Conference on Computer Vision, 1996.](https://mlanthology.org/eccv/1996/kokaram1996eccv-system/) doi:10.1007/3-540-61123-1_175BibTeX
@inproceedings{kokaram1996eccv-system,
title = {{A System for Reconstruction of Missing Data in Image Sequences Using Sampled 3D AR Models and MRF Motion Priors}},
author = {Kokaram, Anil C. and Godsill, Simon J.},
booktitle = {European Conference on Computer Vision},
year = {1996},
pages = {613-624},
doi = {10.1007/3-540-61123-1_175},
url = {https://mlanthology.org/eccv/1996/kokaram1996eccv-system/}
}