Adaptive histogram equalization and its variations

• Published in: Computer vision, graphics, and image processing graphics, and image processing, 1987-09, Vol.39 (3), p.355-368
• Main Authors: Pizer, Stephen M., Amburn, E. Philip, Austin, John D., Cromartie, Robert, Geselowitz, Ari, Greer, Trey, ter Haar Romeny, Bart, Zimmerman, John B., Zuiderveld, Karel
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Computerized, statistical medical data processing and models in biomedicine; General aspects. Methods; Medical sciences
• ISSN: 0734-189X; 1557-895X
• DOI: 10.1016/S0734-189X(87)80186-X

Adaptive histogram equalization (ahe) is a contrast enhancement method designed to be broadly applicable and having demonstrated effectiveness. However, slow speed and the overenhancement of noise it produces in relatively homogeneous regions are two problems. We report algorithms designed to overcome these and other concerns. These algorithms include interpolated ahe, to speed up the method on general purpose computers; a version of interpolated ahe designed to run in a few seconds on feedback processors; a version of full ahe designed to run in under one second on custom VLSI hardware; weighted ahe, designed to improve the quality of the result by emphasizing pixels' contribution to the histogram in relation to their nearness to the result pixel; and clipped ahe, designed to overcome the problem of overenhancement of noise contrast. We conclude that clipped ahe should become a method of choice in medical imaging and probably also in other areas of digital imaging, and that clipped ahe can be made adequately fast to be routinely applied in the normal display sequence.