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 structure that can be exploited for image processing, recognition and analysis. There have been many proposed approaches to the principled statistical modeling of images, but each has been limited in either the complexity of the models or the complexity of the images. 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. Accepted Advanced in Neural Information Processing 10 (1997). 1 Introduction In this paper we describe a multi-scale statistical model which can capture the structure of natural images across many scales. Once trained on example images, it can be used to recognize novel images, or to generate new images. Each of these tasks is reasonably efficient, requiring no more than a few seconds or minutes on a workstation. The sta...