Range limited double-thresholds multi-histogram equalization for image contrast enhancement

Abstract

Thresholding segmentation method is the commonly applied technique for extracting objects from the background of the image. If the single object in the image is clearly distinguishable from the background, the histogram of the image would be bimodal and the threshold can be easily chosen at the bottom of the histogram valley. However, histograms are not always bimodal. Thus, new methods are required to solve this problem. This paper raises a novel contrast enhancement method called range limited double-threshold multi-histogram equalization (RLDTMHE). First, we deduce the Otsu’s double-thresholds method, and divide the image into three parts with the double thresholds. In order to preserve the brightness, range of the equalized image is calculated to yield minimum absolute mean brightness error (AMBE) between the output image and the original one. Finally, each sub-image is equalized independently using the new range. Experiment results show that our algorithm obtains more clear details, while keeping the brightness of the original image very well.