Optik 126 (2015) 2619–2625 Contents lists available at ScienceDirect Optik jo ur nal homepage: www.elsevier.de/ijleo Enhancement of low exposure images via recursive histogram equalization algorithms Kuldeep Singh a,∗ , Rajiv Kapoor b , Sanjeev Kr. Sinha c a Central Research Lab, Bharat Electronics Ltd, Ghaziabad 201010, India b Department of Electronics & Communication, Delhi Technological University, Delhi 110042, India c Department of Computer Science, Indian School of Mines, Dhanbad 826004, India a r t i c l e i n f o Article history: Received 6 May 2014 Accepted 15 June 2015 Keywords: Recursive histogram equalization Image information content Image exposure Low-exposure imaging a b s t r a c t This paper proposes two exposure based recursive histogram equalization methods for image enhance- ment. The proposed methods are very effective for images acquired in low light condition like underwater sequences or night vision images. The first method is recursive exposure based sub-image histogram equalization (R-ESIHE) that recursively performs ESIHE [20] method till the exposure residue among successive iteration is less than a predefined threshold. The second method is named as recursively sep- arated exposure based sub image histogram equalization (RS-ESIHE) that performs the separation of image histogram recursively; separate each new histogram further based on their respective exposure thresholds and equalize each sub histogram individually. The experimental results show that low expo- sure image enhancement problem was not addressed by earlier HE based methods, has been efficiently handled by these new methods. The performance evaluation of new methods is done in terms of image information content as well as visual quality inspection. The proposed methods outperforms earlier HE based contrast enhancement algorithms specifically for low light images. © 2015 Elsevier GmbH. All rights reserved. 1. Introduction Although there is a tremendous advancement in image captur- ing devices, still natural images are often subject to low-exposure problems under low light or underwater conditions. Digital cam- eras have a limited dynamic range as a result photographs acquired in high dynamic range scenes often exhibit underexposure arte- facts in shadow regions [1]. An image captured in a dim light environment encounters low-exposure problem caused by non- ideal camera settings of aperture and shutter speed. Exposure in an image determines the brightness or darkness of each element in the image [2]. In the low illumination scenario, postprocessing using image enhancement tools is needed to improve the qual- ity of the acquired image. Many histogram equalization based image enhancement methods were proposed to cope with contrast related issues. Histogram equalization (HE) is most extensively uti- lized contrast enhancement technique due to its simplicity and ease of implementation [3]. Histogram equalization flattens the probability distribution and stretches the dynamic range of grey ∗ Corresponding author. Tel.: +91 9910101592. E-mail addresses: kuldeep.er@gmail.com (K. Singh), rajivkapoor@dce.ac.in (R. Kapoor). levels, which in result improves the overall contrast of the image [4]. Applying HE straight away on natural images is not suitable for most consumer electronics applications, such as TV, Cameras, etc., as it tends to change the mean brightness of the image to the middle level of the grey level range, which in turn produces annoying artefacts and intensity saturation effects. Kim [4] was the first one to propose an algorithm named brightness preser- ving bi histogram equalization (BBHE), which preserves the mean brightness of the image and improves the contrast. BBHE bisects the histogram based on the input mean brightness and equalizes the two sub histograms independently. Wan et al. [5] proposed an algorithm named dualistic sub image histogram equalization (DSIHE) and claimed that it is better than BBHE in terms of preserva- tion of brightness and information content (entropy) of the image. DSIHE separates the histogram based on median value instead of mean, which implies that each sub histogram contains almost equal number of pixels. Chen and Ramli introduced minimum mean brightness error bi-histogram equalization (MMBEBHE) for preserving the mean brightness “optimally” [6]. MMBEBHE is an extension of BBHE, which iteratively calculates the absolute mean brightness error (AMBE) for grey levels 0 to L-1 and bisects the histogram based on the intensity value X m , which yields mini- mum AMBE. Chen and Ramli [7] proposed another approach named recursive mean-separate histogram equalization (RMSHE). This http://dx.doi.org/10.1016/j.ijleo.2015.06.060 0030-4026/© 2015 Elsevier GmbH. All rights reserved.