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首页> 外文期刊>Optik: Zeitschrift fur Licht- und Elektronenoptik: = Journal for Light-and Electronoptic >Intensity and edge based adaptive unsharp masking filter for color image enhancement
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Intensity and edge based adaptive unsharp masking filter for color image enhancement

机译:基于强度和边缘的自适应模糊图像过滤器,用于彩色图像增强

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摘要

Enhancement of image quality is a fundamental process for a wide range of vision-based applications. Images captured under unfavorable environments are often degraded in information content, sharpness and colorfulness. In the attempts to improve an image, the unsharp masking filter is an attractive candidate for its computational efficiency. However, the filter is vulnerable to the over-range problem where pixel magnitudes are driven beyond permissible ranges. This drawback is particularly noticeable if a non-adaptive procedure is used in the enhancement. Hence, an adaptive gain adjustment method is proposed here aiming at minimizing the number of over-range pixels while maximizing the image sharpness and information content In this method, colorfulness is improved via color channel stretching and contrast is enhanced by edge augmentation. Specifically, a hyperbolic-tangent function, whose scale is dependent on the original image intensity and detected edges, is constructed to adjust the gain in sharpness enhancement. A collection of natural images captured under poor illumination conditions are used in the test against conventional and mask-based image enhancement approaches. Results have demonstrated that the proposed method outperforms the others with regard to colorfulness, information content, and sharpness. (C) 2015 Elsevier GmbH. All rights reserved.
机译:图像质量的提高是各种基于视觉的应用程序的基本过程。在不利环境下拍摄的图像的信息含量,清晰度和色彩通常会下降。在尝试改善图像的过程中,虚化掩膜滤波器因其计算效率而成为有吸引力的候选者。但是,滤波器容易受到超范围问题的影响,在超范围问题中,像素大小被驱动超出允许范围。如果在增强中使用了非自适应过程,则该缺陷特别明显。因此,在此提出一种自适应增益调整方法,其目的是在使图像清晰度和信息含量最大化的同时,使超范围像素的数量最小化。在该方法中,通过色彩通道拉伸来改善色彩,并且通过边缘增强来增强对比度。具体而言,构造双曲线正切函数,其比例取决于原始图像强度和检测到的边缘,以调整清晰度增强中的增益。针对传统和基于掩模的图像增强方法,在测试中使用了在不良光照条件下捕获的自然图像的集合。结果表明,该方法在色彩,信息含量和清晰度方面优于其他方法。 (C)2015 Elsevier GmbH。版权所有。

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