A Non-Parametric Multi-Scale Statistical Model for Natural Images

Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)

Bibtex Metadata Paper

Authors

Jeremy De Bonet, Paul Viola

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.