Computational Intelligence and Neuroscience

Volume 2018, Article ID 3837275, 13 pages

https://doi.org/10.1155/2018/3837275

## Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization

^{1}School of Communication and Information Engineering, Shanghai University, Shanghai, China^{2}Faculty of Electronic and Information Engineering, Huaiyin Institute of Technology, Huai’an, China^{3}Key Laboratory of Advanced Displays and System Application, Ministry of Education, Beijing, China

Correspondence should be addressed to Yepeng Guan; nc.ude.uhs@naugpy

Received 9 May 2018; Accepted 19 July 2018; Published 13 August 2018

Academic Editor: Mario Versaci

Copyright © 2018 Liyun Zhuang and Yepeng Guan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

A novel image enhancement approach called entropy-based adaptive subhistogram equalization (EASHE) is put forward in this paper. The proposed algorithm divides the histogram of input image into four segments based on the entropy value of the histogram, and the dynamic range of each subhistogram is adjusted. A novel algorithm to adjust the probability density function of the gray level is proposed, which can adaptively control the degree of image enhancement. Furthermore, the final contrast-enhanced image is obtained by equalizing each subhistogram independently. The proposed algorithm is compared with some state-of-the-art HE-based algorithms. The quantitative results for a public image database named CVG-UGR-Database are statistically analyzed. The quantitative and visual assessments show that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms. The proposed method can make the contrast of image more effectively enhanced as well as the mean brightness and details well preserved.

#### 1. Introduction

Image contrast enhancement technology is regarded as a classical and important area in image processing. It is widely used in daily photo enhancement, medical image analysis, remote-sensing imagery, microscopic imaging [1], and many other areas [2–6]. Histogram equalization (HE) [7] is most extensively utilized for contrast enhancement. Good contrast images should have the characteristic that the histogram uniformly distributes over the entire range of the intensity. The visual quality of the image is improved by the HE method based on that fact. HE stretches the dynamic range of the histogram by remapping the gray levels on the basis of probability density function (PDF) of the image. In general, the HE has the advantages of efficient computation, quick results, and the usage of real-time applications. Despite these advantages, the HE method has some undesirable effects such as saturation effect, overstretching of input intensities, and so on. It tends to lose the details of image, shift the mean of the input image irrespective of image contents, and disturb the brightness of the image [8].

Substantial HE-based approaches have been developed to overcome the shortcomings of the HE technique in the past decades. However, achieving an enhanced image with high quality in the field of image processing is still a challenging task. In order to more effectively increase the contrast of the input image with brightness and details well preserved, an efficient algorithm named entropy-based adaptive subhistogram equalization (EASHE) is developed in this paper. The proposed method is more effective for preserving the mean brightness and detailed information of the enhanced image while improving the contrast compared with some other state-of-the-art methods. According to the experimental results based on 100 images from CVG-UGR-Database for some state-of-the-art methods and our proposed method, we know that the EASHE technique can achieve the multiple objectives of entropy maximization, details, and brightness preservation as well as control on over enhancement. The main contributions of this paper are as follows: Firstly, we introduce the entropy value-based algorithm to divide the histogram of the input image. Secondly, a novel approach for dynamic range adjustment of image gray level is developed to overcome the grayscale merging and image detailed information missing problems. Thirdly, we put forward a new algorithm to adjust the probability density function of the gray level, which can adaptively control the degree of image enhancement, and the output image looks more natural and clearer. Furthermore, results indicate that the proposed method is a better approach compared with the state-of-the-art methods.

The remainder of this paper is organized as follows: In Section 2, we give an overview of the related work. Section 3 presents the proposed EASHE method. Data samples and performance evaluations are drawn in Section 4. Section 5 provides experimental results and comparisons with state-of-the-art methods, and our concluding remarks are included in Section 6.

#### 2. Related Works

Several HE-based approaches have been reviewed in this section. In order to preserve the mean brightness of the image and improve the contrast, Kim [8] proposes an algorithm named brightness preserving bihistogram equalization (BBHE). It separates the input image histogram based on the input image mean value. DSIHE [9] utilizes input image median to segment histogram, and equal number of pixels are contained in each subhistogram. MMBEBHE [10] is the extension of the BBHE method that provides maximal brightness preservation, which recursively divides the image histogram into multiple groups based on mean brightness error (MBE). DHE [11] partitions histogram based on locations of minima present in the histogram. The span of gray levels in the enhanced image for each subhistogram is decided based on their span in the input image and cumulative frequencies. Though these methods can perform good contrast enhancement, they cause more annoying side effects, including failing with images having nonsymmetric distribution [8], failing to preserve mean brightness [9], producing more annoying side effects [10], and losing structural information [12]. In these techniques, however, the difference between input and output image is minimal, and the desired improvement may not always be achieved [13].

More recently, recursive mean-separate HE (RMSHE [14]) is proposed by Chen and Ramli. The RMSHE further divides the histogram into two parts recursively according to their respective mean value. Each subhistogram is equalized independently by performing BBHE [8], and output image is constructed by the union of all equalized subhistograms. The mean brightness of enhanced image approaches towards the mean brightness of the input image. Sim et al. present another recursively separated (RS) HE method known as recursive subimage HE (RSIHE [15]), which is similar to RMSHE proposed by Chen and Ramli in [14]. RSIHE divides the histogram of the input image based on median values, and 2^{r} subhistograms are generated, where each subhistogram has an equal number of pixels.

In addition to histogram segmentation (i.e., BBHE, DSIHE, RSIHE, etc.), in order to improve HE, histogram clipping also has been developed. Histogram clipping can reduce the domination effect of high frequency bins during HE by controlling the enhancement rate. Examples of histogram clipping-based methods developed by scholars include bihistogram equalization with a plateau limit (BHEPL) [16] and bihistogram equalization median plateau limit (BHEPL-D) [17]. BHEPL is the combination of BBHE and clipped HE. First, the input image is separated by using the mean brightness of image, and then the subhistograms are clipped by using their plateau limits. Then, these subhistograms are separately equalized. The BHEPL-D is similar to the BHEPL, and the difference is that the BHEPL-D clips each subhistogram based on the median of the occupied intensity in the subhistogram.

In [18], Singh et al. recently propose an image enhancement technique using exposure-based subimage histogram equalization (ESIHE). The ESIHE method clips the input histogram at the average number of intensity occurrences and segments the clipped histogram using a threshold based on the image exposure. Singh et al. present a recursive-division-based extension of ESIHE, referred as RS-ESIHE [19]. RS-ESIHE performs recursive divisions of the histogram based on the image exposure. The algorithms based on the recursive division may fail to give natural-looking results due to inappropriate subdivisions. Moreover, deciding the number of division is a critical issue, which may degrade the performance of the algorithm. Singh and Kapoor propose median mean-based subimage clipped histogram equalization MMSICHE [20] algorithm for image enhancement, which firstly performs histogram partition based on median intensity and then divides each subhistograms based on mean intensity.

Additionally, many researchers also propose other HE-based enhancement methods with contrast improvement and brightness and details preservation. For example, modified histogram equalization (MHE) is proposed by Abdullah-Al-Wadud [21]. The proposed MHE approach manipulates the accumulation in the input histogram components before equalizing the histogram. It focuses on preserving the small parts in images. The dynamic histogram specification introduced by Sun et al., which can preserve the shape of the input image histogram, unfortunately, makes limited contrast enhancement [22]. Tsai et al. developed a contrast enhancement algorithm for color images [23, 24]. Huang et al. proposed an adaptive gamma correction with weighting distribution (AGCWD [25]), which enhances the contrast and preserves the overall brightness of an image. In the algorithm, the probability distribution for luminance pixels and the gamma correction is used. The AGCWD approach may not give desired results while it may lose details in the bright regions of image when there are high peaks in the input histogram [26]. Bihistogram equalization using modified histogram bins (BHEMHB) was proposed by Tang and Isa [27], and the algorithm segments the input histogram into two subhistograms according to the median value of the image. BHEMHB alters the histogram bins before HE is applied, but unfortunately limited improvement of contrast is achieved.

#### 3. Proposed Image Enhancement Method

##### 3.1. Entropy-Based Threshold Calculation

The proposed approach provides an optimal division of the original histogram. It is achieved by performing division of the histogram based on the entropy. A subhistogram is divided into two subhistograms with equal entropy. The histogram of an image is divided into four parts with three thresholds which are adaptive and obtained by the same method. The procedure to obtain the thresholds will be presented in detail as follows:

Consider an input image with intensity levels in the dynamic range of , and let be the global histogram of the input image , where and denote lower and uppermost intensities of the image . is the histogram of the gray level , which is defined aswhere is the gray level of in the image . The of the image, , can be described aswhere is the total number of pixels in the input image .

The entropy of can be represented as

The threshold value for histogram segmentation can be obtained: First, we divide the whole histogram into two parts by an adaptive threshold . Then, the two parts can be presented as and . The entropy of can be calculated byThe intensity level is obtained by solvingWe can obtain the threshold by (5), which is utilized to segment the histogram of image. Note that we set , and the optimal thresholds and of the two parts up and down the threshold can also be obtained in the same way as the above. Finally, the histogram is segmented into four subhistograms, that is,where and represent the boundary values of the luminance range within the segmentation. Hence, all subimages are captured byThe input image can be represented as a combination of segmented subimages.

##### 3.2. Segment-Dependent Range Allocation

In Section 3.1, the histogram of the original image is divided into four subhistograms based on the entropy. The gray level intervals are , , , and , respectively. Note that here , , , , and . Usually, most of the existing HE-based approaches equalize subhistograms independently within the original segmentation boundaries. Unfortunately, the HE over narrow ranged subhistograms (having separating points closer to each other) may result in saturation of intensities. On the contrary, HE over widely spaced subhistograms may give rise to uneven expansion of intensities. As a consequence, a resulted image may lose its natural appearance. Therefore, it is necessary to adjust the dynamic range of the subhistogram before the equalization. The process of adjustment is as follows:where is the number of gray levels (i.e., for 8 bits image, *L* = 256) and is the number of subhistograms. is the entropy of the *r*th subhistogram, given aswhere denotes the grayscale range of the subhistogram in the input image histogram, is the total gray level, and represents the dynamic range of the subhistogram in the output image histogram. After adjusting the gray level dynamic range of subhistograms, the gray level range of the image is widely stretched, and the occurrence of grayscale combination is reduced, as shown in Figure 1. We can get the new boundary values of the luminance range within the segmentation formulated as