Abstract
Abstract Four algorithms that modify the histogram equalization algorithm, and extend its capability to a larger range of histograms, are presented. The algorithms allow the user to balance the amount of stretching of peaks in the histogram against the amount of information lost. The effects of the algorithms on peaks or Gaussions having various means, standard deviations and population sizes are discussed. The results are compared to applying local histogram equalization.
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