Histogram shape based Gaussian sub-histogram specification for contrast enhancement

Abstract

Contrast enhancement is an essential primary part of computer vision in all fields of engineering including aerospace, agriculture, electrical, mechanical, medical, surveillance, etc. Annoying side effect in the enhanced images due to variation of gray levels is a key concern in the existing contra st enhancement techniques. In this paper, a new method for contrast enhancement is proposed, which captures the variation in the gray level distribution using skewness of the pixels distribution of the input image to avoid annoying side effects in order to produce contrast enhanced image with entropy close to that of the input image. In the proposed method, the given image is decomposed into two sub-images using a bisecting gray scale value which is determined based on the shape of the histogram of the input image. The desired Gaussian based histograms for sub-images are determined dynamically by controlling the parameters (mean and standard deviation). The performance of the proposed Histogram shape based Gaussian Sub-Histogram Specification technique (HGSHS) is evaluated on the images taken from standard databases and NASA database using the quality metrics: contrast, entropy and gradient. The performance of the proposed technique is found to be better than that of the existing contrast enhancement techniques.