Adaptive Contrast Enhancement Using Gain-Controllable Clipped Histogram Equalization

Adaptive Contrast Enhancement Using Gain-Controllable Clipped Histogram Equalization
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DOI:
10.1109/tce.2008.4711238
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发表时间:
2008-11-01
影响因子:
4.3
通讯作者:
Paik, Joonki
Paik, Joonki
中科院分区:
计算机科学2区
文献类型:
--
作者:
Kim, Taekyung;Paik, Joonki

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直方图均衡化是一种简单有效的对比度增强方法,它可以根据图像的统计特性自动定义灰度变换函数。然而,它往往会改变整个图像的亮度,这是不适合的消费电子产品,其中原始亮度的保存是必不可少的,以避免恼人的文物。针对已有的双直方图均衡化(BHE)和递归均值分离直方图均衡化(RMSHE)方法的不足,提出了一种新的对比度增强方法--增益可控的限幅直方图均衡化(GC-CHE),该方法既能实现直方图均衡化,又能保持图像亮度,并利用增益可控的限幅直方图均衡化实现了Mol-e自适应对比度增强。基于平均亮度来确定限幅率,并且基于限幅率来确定限幅阈值。自适应地控制限幅率以在保持平均亮度的情况下增强对比度。从数学上证明了在自适应控制下,输出图像的平均亮度收敛于输入图像的平均亮度。仿真结果表明,所提出的GC-CHE方法优于现有的基于直方图的方法,如HE,BHE,和RMSHE,在各种情况下的。
Histogram equalization is a simple and effective method for contrast enhancement as it can fine the intensity transformation function automatically define based oil 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, Mol-e 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'.