An Adaptive Histogram Equalization Algorithm on the Image Gray Level Mapping

The conventional histogram equalization algorithm is easy causing information loss. The paper presented an adaptive histogram-based algorithm in which the information entropy remains the same. The algorithm introduces parameter beta in the gray level mapping formula, and takes the information entropy as the target function to adaptively adjust the spacing of two adjacent gray levels in the new histogram. So it avoids excessive gray pixel merger and excessive bright local areas of the image. Experiments show that the improved algorithm may effectively improve visual effects under the premise of the same information entropy. It is useful in CT image processing. (C) 2012 Published by Elsevier B.V. Selection and/or peer-review under responsibility of Garry Lee