Many-level Image Thresholding using a Center-Based Differential Evolution Algorithm

Image thresholding is a crucial image processing task. Most of the time, it plays a pivotal role in an image processing chain, therefore, any error in image thresholding can propagate to other steps such as edge detection, area/volume estimation, or object recognition. Multi-level image thresholding is a popular method for image segmentation, dividing an image into homogeneous regions. Conventional algorithms are timeconsuming due to utilising an exhaustive search, especially when the number of threshold levels increases. On the other hand, population-based metaheuristic algorithms have been successfully applied to this problem. In this paper, we propose a center-based differential evolution (DE) algorithm for high-dimensional multilevel image thresholding (many-level image thresholding). While DE has been shown to yield satisfactory performance for various real-world optimisation problems, in our algorithm, DE is further boosted with a center-based sampling strategy. We evaluate our algorithm on a set of benchmark images on high-dimensional search spaces and with regards to an entropy-based objective function and peak signal-to-noise ratio (PSNR). The obtained results demonstrate that the proposed algorithm can improve upon the performance of other metaheuristic image thresholding techniques.

I. Introduction

Image segmentation plays a fundamental role in machine vision applications and divides an image into non-overlapping groups so that pixels located in the same region share similar characteristics, while pixels from distinct regions exhibit more differences. Image thresholding represents a popular approach to image segmentation due to its simplicity, robustness, and accuracy [1]. Bi-level thresholding selects a single threshold, whereas multi-level thresholding selects multiple thresholds and represents a challenging task that has attracted much research attention in recent years.