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Non-parametric modified histogram equalisation for contrast enhancement

机译:非参数修改直方图均衡以增强对比度

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Histogram equalisation has been a much sought-after technique for improving the contrast of an image, which however leads to an over enhancement of the image, giving it an unnatural and degraded appearance. In this framework, a generalised contrast enhancement algorithm is proposed which is independent of parameter setting for a given dynamic range of the input image. The algorithm uses the modified histogram for spatial transformation on grey scale to render a better quality image irrespective of the image type. Added to this, two variants of the proposed methodology are presented, one of which preserves the brightness of original image while the other variant increases the image brightness adaptively, giving it a better look. Qualitative and quantitative assessments like degree of entropy un-preservation, edge-based contrast measure and structure similarity index measures are then applied to the 500 image data set for comparing the proposed algorithm with several existing state-of-the-art algorithms. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several algorithms.
机译:直方图均衡化是提高图像对比度的一种非常抢手的技术,但是这导致图像的过度增强,从而使其外观不自然且劣化。在此框架中,提出了一种通用的对比度增强算法,该算法与给定输入图像动态范围的参数设置无关。该算法将修改后的直方图用于灰度级的空间变换,以绘制质量更高的图像,而与图像类型无关。除此之外,还提出了所提出方法的两个变体,其中一个变体保留了原始图像的亮度,而另一个变体则自适应地增加了图像的亮度,从而使图像看起来更好。然后对500个图像数据集进行定性和定量评估,例如熵的不保留度,基于边缘的对比度度量和结构相似性指标度量,以将所提出的算法与几种现有的最新算法进行比较。实验结果表明,与几种算法相比,所提出的算法产生的图像效果更好或相当。

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    《Image Processing, IET》 |2013年第7期|641-652|共12页
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