ML Anthology

Efficient Parallel Multigrid Relaxation Algorithms for Markov Random Field-Based Low-Level Vision Applications

Étienne Mémin, Fabrice Heitz, François Charot
CVPR 1994 pp. 644-648
doi:10.1109/CVPR.1994.323786 /cvpr/1994/memin1994cvpr-efficient/

Abstract

We present a new algorithmic framework which enables making a full use of the large potential of data parallelism available on 2D processor arrays for the implementation of nonlinear multigrid relaxation methods. This framework leads to fast convergence towards quasi-optimal solutions. It is demonstrated on two different low-level vision applications.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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Cite

Text

Mémin et al. "Efficient Parallel Multigrid Relaxation Algorithms for Markov Random Field-Based Low-Level Vision Applications." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1994. doi:10.1109/CVPR.1994.323786

Markdown

[Mémin et al. "Efficient Parallel Multigrid Relaxation Algorithms for Markov Random Field-Based Low-Level Vision Applications." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1994.](https://mlanthology.org/cvpr/1994/memin1994cvpr-efficient/) doi:10.1109/CVPR.1994.323786

BibTeX

@inproceedings{memin1994cvpr-efficient,
  title     = {{Efficient Parallel Multigrid Relaxation Algorithms for Markov Random Field-Based Low-Level Vision Applications}},
  author    = {Mémin, Étienne and Heitz, Fabrice and Charot, François},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1994},
  pages     = {644-648},
  doi       = {10.1109/CVPR.1994.323786},
  url       = {https://mlanthology.org/cvpr/1994/memin1994cvpr-efficient/}
}