T. Kim and J. Paik: Adaptive Contrast Enhancement Using Gain-Controllable Clipped Histogram Equalization Contributed Paper Manuscript received August 3, 2008 0098 3063/08/$20.00 © 2008 IEEE 1803 Adaptive Contrast Enhancement Using Gain-Controllable Clipped Histogram Equalization Taekyung Kim and Joonki Paik, Member, IEEE Abstract — Histogram equalization is a simple and effective method for contrast enhancement as it can automatically define the intensity transformation function based on statistical characteristics of the image. However, it tends to alter the brightness of the entire image, which it is not suitable for consumer electronic products, where preservation of the original brightness is essential to avoid annoying artifacts. This paper presents a new contrast enhancement method for generalization of the existing bi- histogram equalization (BHE) and recursive mean-separate histogram equalization (RMSHE) methods. The proposed method is referred to gain-controllable clipped histogram equalization (GC-CHE) to provide both histogram equalization and brightness preservation. More specifically adaptive contrast enhancement is realized by using clipped histogram equalization with controllable gain. The clipping rate is determined based on the mean brightness, and the clipping threshold is determined based on the clipping rate. The clipping rate is adaptively controlled to enhance the contrast with preserving the mean brightness. It is mathematically proven that the mean brightness of the output image converges to that of the input image with adaptive controlled. Simulation results show that the proposed GC-CHE method outperforms existing histogram-based methods, such as HE, BHE, and RMSHE, in various situations 1 . Index Terms — Contrast enhancement, Clipped histogram equalizaion. I. INTRODUCTION Histogram equalization (HE) has been widely used for contrast enhancement of images by uniformly distributing the probability of intensity values. As a result, it flattens and stretches the dynamic range of the original, which improves overall contrast of the image [1]. Applications of histogram equalization (HE) are found in many application areas such as medical image processing, texture synthesis, and speech recognition, to name a few. Recently, its application area has extended to video enhancement for digital broadcasting and internet-based streaming. 1 This work was supported by Seoul Future Contents Convergence (SFCC) Cluster established by Seoul R&BD Program and by Korean Ministry of Information and Communication under the Chung-Ang University HNRC- ITRC program by the Chung-Ang University Excellent Researcher Grant in 2008. Taekyung Kim is with the Department of Image Engineering, Chung-Ang University, Seoul, Korea. (e-mail: kimktk@wm.cau.ac.kr ). Joonki Paik is with the Department of Image Engineering, Chung-Ang University, Seoul, Korea. (e-mail: paikj@cau.ac.kr ). In spite of its fundamental advantage, HE has a significant drawback of changing the brightness globally, which results in either under-saturation or over-saturation of important regions. From this reason, for the implementation of contrast enhancement in consumer electronics, it is advised that the lost intensity values by the histogram processing should be minimized in the output image [2]. The first challenge of modified histogram has been proposed by using bi- histogram equalization (BHE) [2]. In this method, based on the mean value the histogram is divided into two groups, which are independently equalization. It has been analyzed both mathematically and experimentally that this technique can better preserve the original brightness to a certain extent. An alternative approach, referred as dualistic sub-image histogram equalization (DSHE), has been proposed by Wan et al [3]. DSHE is similar to BHE, except that histogram separation is based on median value instead of the mean value. The threshold of the separation is chosen such that two histograms have the equal number of pixels. It is claimed that DSHE is better than BHE in terms of preserving both brightness and entropy of the input image. Each histogram with cumulative probability density of 5 . 0 guarantees the maximum entropy in the output image. In spite of evenly distributed histograms BHE and DSHE cannot meet the higher degree of brightness preservation to avoid undesired artifacts. Chen and Ramli have proposed an improved contrast enhancement scheme referred as recursive mean-separate histogram equalization (RMSHE) [4]. This technique iteratively performs the BHE, where the mean of each sub- histograms is calculated, and sub-histograms are then further divided into two parts based on this mean values. This process is repeats for a prespecified number of n times. Thus, this technique will produce separately equalized n 2 sub-histogram. It is claimed that RMSHE has good brightness preservation technique when the number of iterations is large, because the output mean converges to the input mean. However, too many number of iterations result in null processing, where this condition, the output image is exactly the copy of the input image. In addition to drawbacks of individual methods, the common challenge to histogram-based methods is the amplification of noise along with the enhanced contrast of the image. Noise amplification results in significant degradation of image quality. Accordingly, without a priori information of noise behavior, the performance of contrast enhancement is limited to a certain degree.