Peer group image enhancement

Peer group image processing identifies "peer group" for each pixel and then replaces the pixel intensity with the average over the peer group. Two par ameters provide direct control over which image features are selectively en hanced: area (number of pixels in the feature) and window diameter (window size needed to enclose the feature). A discussion is given of how these par ameters determine which features in the image are smoothed or preserved. We show that the Fisher discriminant can be used to automatically adjust the PGA parameters at each point in the image. This local parameter selection a llows smoothing over uniform regions while preserving features like corners and edges. This adaptive procedure extends to multilevel and color forms o f PGA, Comparisons are made with a variety of standard filtering techniques and an analysis is given of computational complexity and convergence issue s.