On advances in statistical modeling of natural images
Anuj Srivastava ,
Ann B Lee ,
Eero P Simoncelli
and
Song-Chun Zhu,
Published in:
Journal of Mathematical Imaging and Vision
18(1): 17-33, January 2003.
© Kluwer Academic Publishers.
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.
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