Digital Processing Of Nonstationary Images Using Local Autocovariance Statistics
Robin N. Strickland · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1984
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.