A GRAY SCALE IMAGE SEGMENTATION TECHNIQUE USING HISTOGRAM EQUALIZATION BASED FIREFLY ALGORITHM

Abstract

The segmentation of images is one of the essential processing phases, in which an image is separated into several parts. Histogram equalization is a technique which generates a kind of threshold values that can be used for the segmentation of the image. It remains a difficult problem to determine the wide range of thresholds and their values. The suggested optimization strategy for the Firefly algorithm (HEFA) is used to exercise large variation values for segmentation techniques with the best number of threshold rates. The mostly firefly-based Histograms Equalization is used to access the multi-threshold looking at maximum entropy, while additional statistics on the finest image segment are observed with the optimisation objective function. Our proposed HEFA technological features have better convergence rates, providing a reasonable segmental value of PSNR and SSIM, based on experimental results in contrast to the CSO and FA techniques. The experimental results are shown to be the higher-segmented version of the proposed HEFA technique.