A Novel Brightness Preserving Histogram Equalization Technique for Image Contrast Enhancement

A histogram is a graphical representation of the distribution of data in an image. It is an estimate of the probability distribution of a continuous variable (quantitative variable). Histogram Equalization is a contrast enhancement technique in the image processing which uses the histogram of image. However histogram equalization is not the best method for contrast enhancement because the mean brightness of the output image is significantly different from the input image. There are several extensions of histogram equalization has been proposed to overcome the brightness preservation challenge. Contrast enhancement using brightness preserving bi-histogram equalization (BBHE) which divides the image histogram into two parts based on the input mean and median respectively then equalizes each sub histogram independently. This research paper aims at providing the results of the algorithms of image enhancement techniques like Brightness Preservation Bi-Histogram Equalization (BBHE), Brightness Preserving Dynamic Histogram Equalization (BPDHE) and Combined Approach and determine the best one for image enhancement. The comparison of these techniques is done on the basis of different applications like medical imaging, consumer electronics etc. and for evaluation of these techniques, various performance parameters are calculated like Mean Square Error (MSE), Normalized Absolute Error (NAE), Correlation, Peak Signal to Noise Ratio (PSNR) and Correlation for gray scale images. These are the objective measures of evaluation and the subjective evaluation is done on the basis of Visual Quality of the images. All the enhancement techniques are implemented using MATLAB-2015 and its image processing toolbox. Enhancement techniques are applied on images of different size like 512 512, 256 256, etc and from different application fields like real images, medical images and consumer electronics images.