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A number of locally-adaptive, non-linear techniques for image enhancement have recently been developed. These methods typically involve convolution between the image and a rectangular, fixed-size, sliding window positioned over each pixel in the image whose coefficient are dependent on the image statistics under the sliding window. These nonlinear techniques, however, share a fundamental shortcoming that can reduce their utility: they are based on an assumption of stationarity of the image. This is, in general, not a good assumption, as image characteristics often change abruptly. Image segmentation can be used to break up an image into relative stationary regions so that different types of filters may be applied to statistically different regions of the image, however, this can result in unwarranted enhancement of edges between segmented regions. In this paper we present a new paradigm for image processing operations where unlike fixed-neighborhood methods, enhancement operations are based on the characteristics of an adaptive neighborhood determined individually for each pixel in the image. The adaptive neighborhood, just like the fixed neighborhood, surrounds the pixel to be enhanced, but the shape and area covered by the adaptive neighborhood are dependent on the local characteristics of the image rather than being arbitrarily defined. Images enhanced using adaptive neighborhoods are superior to those enhanced using fixed neighborhoods, as the adaptive-neighborhood image processing (ANIP) techniques tune themselves to the contextual details in the image. A major advantage in the adaptive-neighborhood techniques is that edges in the images are not degraded since the adaptive neighborhoods tend not to transcend real edges or boundaries in the image. We have, over the past decade, presented a series of articles describing various adaptive-neighborhood enhancement techniques in which neighborhoods are allowed to overlap. In our experience images enhanced using adaptive, overlapping neighborhoods are superior to those enhanced using fixed neighborhoods, as the adaptive-neighborhood techniques tune themselves to the contextual details in the image. In this paper, we provide an overall unifying viewpoint for this work, so that the use of adaptive-neighborhood image processing may be viewed as a new paradigm for image processing operations.
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