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

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

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Jeremy De Bonet, Paul Viola


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.