Elevating image segmentation with multilevel two-dimensional quantum representation

In the rapidly advancing field of image analysis and processing, accurately segmenting images into meaningful regions remains a critical challenge. Drawing from recent advancements in quantum computing and information theory, our research introduces an innovative approach to image segmentation. This work presents a novel multilevel segmentation method that utilizes a two-dimensional quantum image representation, offering a more sophisticated and efficient technique for image thresholding. In this framework, the image's 2D histogram is treated as a quantum system, with quantum Rényi entropy used to quantify the information contained within the image. To enhance segmentation quality, we first improve the contrast of the images by applying a new contrast enhancement algorithm before performing the segmentation. The resulting entropy-based fitness function is then optimized using Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms to determine the optimal thresholding values. A comprehensive comparative analysis is conducted between the proposed quantum method and traditional classical approaches, evaluated on a set of benchmark images using nine metrics, including the Wilcoxon test for statistical significance. Experimental results demonstrate the effectiveness of the PSO optimizer, the superiority of the two-dimensional quantum image representation.