On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and VisionPublished 1 January 2003
Anuj Srivastava, A. B. Lee, Eero P. Simoncelli, Shaoyang Zhu
Citations492
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TL;DR
Some recent results in statistical modeling of natural images that attempt to explain patterns of non-Gaussian behavior of image statistics, i.e. high kurtosis, heavy tails, and sharp central cusps are reviewed.
Abstract
Statistical analysis of images reveals two interesting properties: (i) invariance of image statistics to scaling of images, and (ii) non-Gaussian behavior of image statistics, i.e. high kurtosis, heavy tails, and sharp central cusps. In this paper we review some recent results in statistical modeling of natural images that attempt to explain these patterns. Two categories of results are considered: (i) studies of probability models of images or image decompositions (such as Fourier or wavelet decompositions), and (ii) discoveries of underlying image manifolds while restricting to natural images. Applications of these models in areas such as texture analysis, image classification, compression, and denoising are also considered.
Keywords
Computer Science
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