A weight-based optimization algorithm of histogram equalization
Juan Chen, Quan Wen, Shijie Zhou · 2019
Histogram equalization is a simple and effective image enhancement technique that adjusts the contrast through the histogram of the image. In order to optimize the histogram equalization and improve the conventional mapping method, we propose the Histogram Equalization of Weighted Gray-Level Difference (HEWGLD) algorithm, which utilizes the quantity of pixels at each gray level as weight and adjusts the image gray levels based on the conventional histogram equalization results. The whole problem is modelled as a linear programming problem, and solved by a greedy method, which can lead to the global optimal value. The experimental results show that compared with the conventional histogram equalization algorithm, the optimization algorithm has obvious contrast enhancement effect for grayscale images with histogram peaks, and the visual effects of the edges between foreground and background in the image are improved efficiently.