Edge-affected context for adaptive contrast enhancement

Abstract

Contrast enhancement is a fundamental step in the display of digital images. The end result of display is the perceived brightness occurring in the human observer; design of effective contrast enhancement mappings therefore requires understanding of human brightness perception. Recent advances in this area have emphasized the importance of image structure in determining our perception of brightnesses, and consequently contrast enhancement methods which attempt to use structural information are being widely investigated. In this paper we present two promising methods we feel are strong competitors to presently-used techniques. We begin with a survey of contrast enhancement techniques for use with medical images. Classical adaptive algorithms use one or more statistics of the intensity distribution of local image areas to compute the displayed pixel values. More recently, techniques which attempt to take direct account of local structural information have been developed. The use of this structural information, in particular edge strengths, in defining contextual regions seems especially important. Two new methods based on this idea are presented and discussed, namely edge-affected unsharp masking followed by contrast-limited adaptive histogram equalization (AHE), and diffusive histogram equalization, a variant of AHE in which weighted contextual regions are calculated by edge-affected diffusion. Results on typical medical images are given.