Digital Processing Of Nonstationary Images Using Local Autocovariance Statistics

Robin N. Strickland

doi:10.1117/12.944874

4 December 1984 Digital Processing Of Nonstationary Images Using Local Autocovariance Statistics

Robin N. Strickland

Proceedings Volume 0504, Applications of Digital Image Processing VII; (1984) https://doi.org/10.1117/12.944874
Event: 28th Annual Technical Symposium, 1984, San Diego, United States

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.

Citation Download Citation

Robin N. Strickland "Digital Processing Of Nonstationary Images Using Local Autocovariance Statistics", Proc. SPIE 0504, Applications of Digital Image Processing VII, (4 December 1984); https://doi.org/10.1117/12.944874

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KEYWORDS

Image processing

Data modeling

Digital image processing

Image compression

Convolution

Image restoration

Error analysis

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