Exposure Based Multi-Histogram Equalization Contrast Enhancement for Non-Uniform Illumination Images

Abstract

Non-uniform illuminated images pose challenges in contrast enhancement due to the existence of different exposure region caused by uneven illumination. Although Histogram Equalization (HE) is a well-known method for contrast improvement, however, the existing HE-based enhancement methods for non-illumination often generated the unnatural images, introduced unwanted artifacts, and washed out effect because they do not utilize the information from the different exposure regions in performing equalization. Therefore, this study proposes a modified HE-based contrast enhancement technique for non-uniform illuminated images namely Exposure Region-Based Multi-Histogram Equalization (ERMHE). The ERMHE uses exposure region-based histogram segmentation thresholds to segment the original histogram into sub-histograms. With the thresholded sub-histograms, the ERMHE then uses an entropy-controlled gray level allocation scheme to allocate new output gray level range and to obtain new thresholds that will be used to repartition the histogram prior to HE process. A total of 154 non-uniform illuminated sample images are used to evaluate the application of the proposed ERMHE. By comparing ERMHE to four existing HE-based contrast enhancement namely, Global HE, Mean Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE), and Contrast Limited Adaptive Histogram Equalization (CLAHE), qualitatively, the ERMHE produces enhanced images with a natural appearance, appealing contrast, less degradation, and reasonable detail preservation. Quantitatively, the ERMHE achieves the highest peak signal-to-noise-ratio (PSNR), lowest Absolute Mean Brightness Error (AMBE), and second best in Discrete Entropy (DE) scores. From the analyses, the ERMHE has shown its capability in enhancing different exposure regions exist in non-uniform illuminated images.