Statistical Models for Images: Compression, Restoration and Synthesis (1997)
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| Venue: | In 31st Asilomar Conf on Signals, Systems and Computers |
| Citations: | 116 - 31 self |
BibTeX
@INPROCEEDINGS{Simoncelli97statisticalmodels,
author = {Eero Simoncelli},
title = {Statistical Models for Images: Compression, Restoration and Synthesis},
booktitle = {In 31st Asilomar Conf on Signals, Systems and Computers},
year = {1997},
pages = {673--678},
publisher = {IEEE Computer Society}
}
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Abstract
this paper, we examine the problem of decomposing digitized images, through linear and/or nonlinear transformations, into statistically independent components. The classical approach to such a problem is Principal Components Analysis (PCA), also known as the Karhunen-Loeve (KL) or Hotelling transform. This is a linear transform that removes second-order dependencies between input pixels. The most well-known description of image statistics is that their power spectra take the form of a power law [e.g., 20, 11, 24]. Coupled with a constraint of translationinvariance, this suggests that the Fourier transform is an appropriate PCA representation. Fourier and related representations are widely used in image processing applications.
