A parametrized family of nonlinear image smoothing filters

TitleA parametrized family of nonlinear image smoothing filters
Publication TypeJournal Article
Year of Publication1989
AuthorsRey, C., and R. K. Ward
JournalAcoustics, Speech and Signal Processing, IEEE Transactions on
Pagination1458 -1462
Date Publishedsep.
Keywordsedge retention, filtering and prediction theory, gray level, noise smoothing, nonlinear image smoothing filters, nonlinear weights, parametrized family, picture processing

A parameterized family of nonlinear image smoothers is developed which provides a range of tradeoffs between noise smoothing and edge retention. The presentation unifies many approaches to the problem of image smoothing where the estimate of the gray level of a pixel is taken as a nonlinear data-dependent weighted sum of the gray levels of the pixel's neighborhood. Local confidence measures are defined, and it is shown how filters based on the sample median, the absolute gradient, and the sample variance incorporate these confidence measures in their nonlinear weights. The notion of localized sample variance is then introduced and shown to constitute a more appropriate confidence measure. Using the localized sample variances, a family of filters termed LVn is derived. Smaller values of n provide better noise removal, whereas higher values of n provide better edge preservation. Experiments indicate that the LV 2 member of the family is very efficient for noise removal, while the extreme member LV infin; is nearly perfect for edge retention. A good tradeoff is achieved using n=4, 5, or 6. These values give the most aesthetically appealing results and yield lower RMS error than those of other filters discussed


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