ML Anthology

Robust Bayesian Estimation and Normalized Convolution for Super-Resolution Image Reconstruction

Antonis Katartzis, Maria Petrou
CVPR 2007
doi:10.1109/CVPR.2007.383429 /cvpr/2007/katartzis2007cvpr-robust/

Abstract

We investigate new ways of improving the performance of Bayesian-based super-resolution image reconstruction by using a discontinuity adaptive image prior distribution based on robust statistics and a fast and efficient way of initializing the optimization process. The latter is an adapted normalized convolution (NC) technique that incorporates the uncertainty induced by registration errors. We present both qualitative and quantitative results on real video sequences and demonstrate the advantages of the proposed method compared to conventional methodologies.

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Cite

Text

Katartzis and Petrou. "Robust Bayesian Estimation and Normalized Convolution for Super-Resolution Image Reconstruction." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383429

Markdown

[Katartzis and Petrou. "Robust Bayesian Estimation and Normalized Convolution for Super-Resolution Image Reconstruction." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/katartzis2007cvpr-robust/) doi:10.1109/CVPR.2007.383429

BibTeX

@inproceedings{katartzis2007cvpr-robust,
  title     = {{Robust Bayesian Estimation and Normalized Convolution for Super-Resolution Image Reconstruction}},
  author    = {Katartzis, Antonis and Petrou, Maria},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383429},
  url       = {https://mlanthology.org/cvpr/2007/katartzis2007cvpr-robust/}
}