Iris recognition is one of the most prevalent methods for personal identification in the modern day. Low quality iris images that are blurry, of low resolution, and poor illumination poses a big challenge for iris recognition as the iris recognition efficiency is entirely dependent on whether the image supplied is of good quality. Therefore, several enhancement techniques have been proposed and used for image processing to increase the quality of iris images. Although there is no best approach for all types of image enhancement, histogram equalization (HE) is a commonly used approach as it is a simple yet effective method. In this paper, several enhancement techniques based on histogram equalization approaches, such as histogram equalization (HE), brightness preserving bihistogram equalization (BBHE), dynamic histogram equalization (DHE), adaptive histogram equalization (AHE), contrast limited adaptive histogram equalization (CLAHE), dualistic sub-image histogram equalization (DSIHE), and multi-scale adaptive histogram equalization (MAHE), have been explored. A comparison is conducted based on the image quality of all image enhancement techniques proposed in this paper.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.