Spatial Information Computation-Based Low Contrast Image Enhancement

Abstract

This paper proposes a novel method of image enhancement via energy curve equalization (ECE). Histogram equalization (HE) appears to be the most straightforward method for image contrast enhancement. Many modifications are already recommended to overcome the confines of HE. The computation of the histogram does not consider the spatial correlations among the surrounding pixels. The current method utilizes the energy curve as a substitute for the histogram of an image. In contrast to the histogram, this approach covers the spatial context information of neighboring pixels. The modified Hopfield neural network (HNN) architecture is employed to compute an image's energy curve. It has peaks and valleys and appears smoother than the image histogram. The average value of the mean and median of the energy curve is used as the plateau limit to control the enhancement rate. The clipped energy curve is partitioned into three individual regions based on the standard deviation value. The final transformation function is utilized to remap the pixel intensity values and is formulated by integrating the individual portions of equalized energy curves. The simulation results confirm the projected technique's effectiveness compared to conventional HE-based methods with and without plateau limit.