A Novel Approach for Contrast Enhancement using Image Classification and Subdivision based Histogram Equalization

In the field of image processing, contrast enhancement is a vital area to enhance contrast of images which are having poor contrast. Histogram Equalization (HE) is the method used to increase contrast in images. To improve contrast in images several HE methods persist. By determining the Probability Density Function (PDF) and Cumulative Density Function (CDF), HE expands the distribution of pixels. The overall brightness would be altered while the histogram equalization is being applied is a one kind of disadvantage. The drawback which is mentioned here can be avoided by, classifying the image based on intensity exposure and is divided into sub images based on the median value. To minimize the over enhancement, the sub images are clipped using the threshold value. Equalization can be done separately for each sub images, and the equalized sub images are combined to form a single image. Thus, to keep the brightness and to bring limitation in enhancement rate the classification and subdivision based HE method was proposed which equalizes the image. For color images, this method of equalization performs better than gray scale images. The simulation results for several test images are obtained using Matlab software tool. The results show that the entropy of the proposed method is compared with the standard HE method and it determines the amount of information available in the image. The proposed method provides better enhancement than other methods of equalization by controlling the enhancement rate. Improvements can be done by selecting the threshold values for clipping and intensity exposure. Contrast enhancement is applied in the areas of photography, medical imaging and video surveillance systems to enhance quality in images and the image looks natural.