Adaptive Histogram Equalization And Its Applications

This paper describes an efficient array-processor implementation of an adaptive histogram equalization algorithm for digital image enhancement. The algorithm is based on a sliding window approach, and computes local histograms and grey level mappings for generating uniform (equalized) histograms for each pixel location. Equivalently, this method can be interpreted as generating local maximum entropy representations of the original image data. For sample digital imagery, it is shown that on the average, a 62% increase in local entropy can be obtained. In addition, the effects of adjusting key parameters (such as local brightness, gain, etc.) upon processed imagery are discussed. The technique has been applied to the analysis of high quality digital imagery and found to be particularly effective for accentuating subtle texture and detail in the data.