Adaptive Bi-Histogram Equalization Using Threshold (ABHET)
Contrast enhancement and brightness preservation of the image are two important issues of image enhancement in research field now-a-days. The objective is to enhance the image uniformly over different parts of the image. General Histogram Equalization doe
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Department of ECE, S ‘O’ A University, Bhubaneswar, Odisha, India [email protected], [email protected], [email protected]
Abstract. Contrast enhancement and brightness preservation of the image are two important issues of image enhancement in research field now-a-days. The objective is to enhance the image uniformly over different parts of the image. General Histogram Equalization doesn’t control degree of enhancement of the image. To overcome this drawback, another variant of Histogram Equalization method namely Adaptive Bi-histogram Equalization using Threshold (ABHET) is being proposed. The proposed method undergoes three steps, such as: Histo‐ gram segmentation using threshold, Clipping of histogram using mean value of occupied intensity and histogram equalization of each sub-images. Finally all the sub-images are combined into one complete image. Simulation results show that ABHET outperforms other existing HE-based methods and different image quality measures such as: Peak signal to noise ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Structural Similarity Index (SSIM) are being used to test the robustness of the proposed method in terms of enhancement of contrast and preservation of brightness. Keywords: Contrast enhancement · Histogram equalization · AIEBHE · AIEBHE-AHE · AHE-AIEBHE · AQHEMT · ABHET
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Introduction
The objective of image enhancement is to improve the visual appearance of the image or to provide better enhanced image for further processing. It is difficult to capture good quality picture in darkness or in low light. Also in darkness small details of the object can not be perceived. In order to get better quality image different enhancement tech‐ niques are being used. Histogram equalization (HE) is a very popular method of contrast enhancement technique among different contrast enhancement techniques exist. Hence HE improves the overall contrast of the image and preserves mean brightness [1, 2]. HE has different applications such as medical imaging system (X-ray), texture synthesis and video enhancement etc. But traditional HE is not suitable for consumer electronics systems [2]. This causes over enhancement, noise amplification, saturation effect etc. These are some of the biggest demerits of traditional HE.
© Springer Nature Singapore Pte Ltd. 2016 A. Unal et al. (Eds.): SmartCom 2016, CCIS 628, pp. 151–158, 2016. DOI: 10.1007/978-981-10-3433-6_19
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Related Literature
For the enhancement of image, many methods can be involved for different purpose such as, boundary detection is to enhance the boundary of the image. This has been approached by many authors [4]. Some of the equalization based methods used for such purpose are also given in this section. In [2], Kim proposed a technique namely Bright‐ ness Preserving Bi- HE (BBHE) for preserving brightness and enhancement of contrast. This technique sub-divide the gray image utilizing input gray image mean brightness value finally equalizes sub-histograms individually. In [5] Wan et a
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