Social Spider Optimization Based Optimally Weighted Otsu Thresholding for Image Enhancement

Abstract

Image enhancement corresponds to processing an image to obtain an image with more perceptual details. In this paper, a social spider optimization (SSO) based scheme is proposed to generate an enhanced image which contains higher contrast and minimum change of entropy with respect to the original image. The proposed method employs histogram equalization with a modified cumulative distribution function to obtain a mapping function. A three-step process is followed to modify the original histogram. The first step is to segment the image histogram into two parts using the Otsu's thresholding method. Then both of the upper and lower histograms are weighted as well as thresholded to control the level of enhancement. The constraint parameters for modification are obtained by SSO. After applying the constrain parameters on the histograms, mean shift correction is performed to ensure there is a minimum level of mean shift from input image to output image. The results indicate that proposed method achieves better color preservation, and balanced contrast enhancement in comparison to existing techniques. The proposed scheme also leads to significant feature enhancement, low contrast boosting, and brightness preservation in the enhanced image, while preserving the natural feel of the original image.