Image contrast enhancement based on the Schrödinger operator spectrum
This is an incremental improvement for image processing applications, enhancing contrast while preserving original characteristics.
The authors tackled image contrast enhancement by projecting images onto the squared eigenfunctions of the 2D Schrödinger operator, using parameters optimized via NSGA-II and priors, resulting in effective enhancement with minimal artifacts.
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.