A Non-Parametric Multi-Scale Statistical Model for Natural Images
Abstract
The observed distribution of natural images is far from uniform. On the contrary, real images have complex and important struc(cid:173) ture that can be exploited for image processing, recognition and analysis. There have been many proposed approaches to the prin(cid:173) cipled statistical modeling of images, but each has been limited in either the complexity of the models or the complexity of the im(cid:173) ages. We present a non-parametric multi-scale statistical model for images that can be used for recognition, image de-noising, and in a "generative mode" to synthesize high quality textures.
Cite
Text
De Bonet and Viola. "A Non-Parametric Multi-Scale Statistical Model for Natural Images." Neural Information Processing Systems, 1997.Markdown
[De Bonet and Viola. "A Non-Parametric Multi-Scale Statistical Model for Natural Images." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/bonet1997neurips-nonparametric/)BibTeX
@inproceedings{bonet1997neurips-nonparametric,
title = {{A Non-Parametric Multi-Scale Statistical Model for Natural Images}},
author = {De Bonet, Jeremy S. and Viola, Paul A.},
booktitle = {Neural Information Processing Systems},
year = {1997},
pages = {773-779},
url = {https://mlanthology.org/neurips/1997/bonet1997neurips-nonparametric/}
}