2024/06/04 by Vargas, Juan M., Laleg-Kirati, Taous-Meriem
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences
paper · doi:10.48550/arxiv.2406.02264
In this study, we propose a novel image contrast enhancement method based on projecting images onto the squared eigenfunctions of the two-dimensional Schrödinger operator. This projection relies on a design parameter, γ, which controls pixel intensity during image reconstruction. The method's performance is evaluated using color images. The selection of γ values is guided by priors based on fuzzy logic and clustering, preserving the spatial adjacency information of the image. Additionally, multi-objective optimization using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to determine the optimal values of γ and the semi-classical parameter, h, from the 2D-SCSA. Results demonstrate that the proposed method effectively enhances image contrast while preserving the inherent characteristics of the original image, producing the desired enhancement with minimal artifacts.