In order to improve the drawback of brightness preserving bi-histogram equalization (BHE) algorithms

a dynamic range optimization method based on maximum entropy model is proposed to extend the application range of BHE algorithms

which makes it not only suitable for normal brightness image

but also can get good effect on low illumination and high brightness image.Firstly

the segmentation point decided by Otsu divides the original histogram into two sub-histograms.And then

a hybrid adjustable method is proposed to pre-process the initial histogram.After that

based on the proposed maximum entropy model

the best dynamic range segmentation point is determined by an ergodic optimization method.Finally

the BHE procedure outputs the final contrast enhanced image.Multiple image databases are selected for testing and compared with BBHE (Brightness preserving Bi-Histogram Equalization)

BPCLBHE (Brightness Preserving and Contrast Limited Bi-Histogram Equalization)

ESIHE (Exposure based Sub Image Histogram Equalization) and DRSHE (Dynamic Range Separate Histogram Equalization)

and we use the entropy

contrast and NIQE (Natural Image Quality Evaluator) as objective evaluation indexes.The experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art BHE algorithms on almost all types of images

with better subjective visual effective and better objective score of quality evaluation

and it takes the enhancement of contrast into account while preserving details.