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Proceedings Paper

Digital Processing Of Nonstationary Images Using Local Autocovariance Statistics
Author(s): Robin N. Strickland
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Paper Abstract

This paper addresses the problem of local/spatially-variant/adaptive image processing based on direct estimates of local autocovariance functions. In order to quantify the non-stationarity of images, and often, to implement spatially-variant processing, we require estimates or measurements of the local image statistics, specifically the autocovariance function. The simplest way to achieve this is to divide the image into N x N - pixel sub-blocks (e.g. N = 16), and calculate the usual biased or unbiased autocovariance function of each sub ock. In effect, each subblock is treated as part of a wide-sense stationary field. It is well-known, however, that reliable power spectral estimates require much larger amounts of data. Nevertheless, as our work shows, it is possible to obtain useful maps of local autoco-variance parameters if we assume simple parametric autocovariance models. Specifically, we employ popular first-order models, such as the nonseparable exponential model. We discuss a procedure for estimating local autocovariance parameters. The resulting parameters are seen to correlate with observed signal activity. We also outline techniques for spatially-variant image processing - coding, restoration, and enhancement - based on local statistics. Processed examples are given.

Paper Details

Date Published: 4 December 1984
PDF: 12 pages
Proc. SPIE 0504, Applications of Digital Image Processing VII, (4 December 1984); doi: 10.1117/12.944874
Show Author Affiliations
Robin N. Strickland, University of Arizona (United States)

Published in SPIE Proceedings Vol. 0504:
Applications of Digital Image Processing VII
Andrew G. Tescher, Editor(s)

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