Histogram modification in adaptive bi-histogram equalization for contrast enhancement on digital images

Abstract

In this paper, a novel histogram modification-based bi histogram equalization (HE) approach for contrast enhancement on digital images is presented. At first, a power-logarithm transformation function is used to change the histogram of the input image. The logarithm operation reduces the input histogram’s excessive peaks, while the power function restores the histogram structure. The adjusted histogram is then separated into two sub-histograms at the threshold limit, using the input image’s minimum intensity and standard deviation. Sub-histograms are clipped based on their individual plateau limit parameters, which are based on the median value of the individual sub-histogram, to control the over-enhanced outcomes. The clipped pixels are then redistributed evenly across the histogram’s non-empty bins. Finally, the intended outcome is achieved by applying the HE procedure on the updated individual sub-histogram. Simulation results show that the proposed HE approach successfully improves the visual quality of the images. Quantitative measurements such as entropy, feature similarity index measure (FSIM), spectral residual similarity index measure (SR-SIM), gradient magnitude similarity deviation (GMSD), visual saliency-induced index (VSI) and multi scale structural similarity index measure (MS-SSIM) efficiently confirm the effectiveness of the suggested method when compared with the existing enhancement techniques. Furthermore, a comparative study of the different approaches is performed using a dataset of 300 images, demonstrating the superiority of the suggested method over the other state-of-the-art techniques.