ML Anthology

Robust Super-Resolution

Assaf Zomet, Alex Rav-Acha, Shmuel Peleg
CVPR 2001 pp. I:645-650
doi:10.1109/CVPR.2001.990535 /cvpr/2001/zomet2001cvpr-robust/

Abstract

A robust approach for super-resolution is, presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion errors, inaccurate blur models, noise, moving objects, motion blur etc. This robustness is needed since super-resolution methods are very sensitive to such errors. A robust median estimator is combined in an iterative process to achieve a super resolution algorithm. This process can increase resolution even in regions with outliers, where other super resolution methods actually degrade the image.

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Cite

Text

Zomet et al. "Robust Super-Resolution." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990535

Markdown

[Zomet et al. "Robust Super-Resolution." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/zomet2001cvpr-robust/) doi:10.1109/CVPR.2001.990535

BibTeX

@inproceedings{zomet2001cvpr-robust,
  title     = {{Robust Super-Resolution}},
  author    = {Zomet, Assaf and Rav-Acha, Alex and Peleg, Shmuel},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2001},
  pages     = {I:645-650},
  doi       = {10.1109/CVPR.2001.990535},
  url       = {https://mlanthology.org/cvpr/2001/zomet2001cvpr-robust/}
}