We investigate the natural requirement that an automated contrast enhancement algorithm be idempotent. We show that in a restricted but natural setting, it leads to a binding of the contrast changes with the decision procedure. We obtain: a natural decision procedure for the standard gamma-transformations; a class of contrast changes generalizing these; a procedure to "learn" an algorithm from a set of good images.
Idempotence and automatic linear contrast enhancements
2005-01-01
400529 byte
Conference paper
Electronic Resource
English
Idempotence and Automatic Linear Contrast Enhancements
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