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首页> 外文会议>Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII pt.1 >Adaptive Histogram Subsection Modification for Infrared Image Enhancement
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Adaptive Histogram Subsection Modification for Infrared Image Enhancement

机译：自适应直方图分段修改以增强红外图像

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摘要

Firstly, the drawbacks of infrared image histogram equalization and its improved algorithm are analyzed. A novel technique which can not only enhance the contrast but also preserve detail information of infrared image is presented. It is called adaptive histogram subsection modification in this paper. The property of infrared image histogram is applied to determine the subsection position adaptively. The second-order differential coefficient of gray level probabilistic density curve is calculated from top down direction. The first inflexion is chosen as the subsection point between high probabilistic density gray levels and low probabilistic density gray levels in the histogram of infrared image. Then the histogram of low probabilistic density section and high probabilistic density section are mapped and modified respectively. Finally, subsection images are combined together and an output infrared image is reconstructed. The contrast is enhanced and the original gray levels are mostly preserved simultaneously during extending the dynamic range of gray levels in infrared image. Meanwhile, suitable distance is kept between gray levels to avoid large isolated grains defined as patchiness in the image. Several infrared images are adopted to demonstrate the performance of this method. Experimental results show that the infrared image quality is greatly improved by this approach. Furthermore, the proposed algorithm is simple and easy to perform.
机译：首先，分析了红外图像直方图均衡化的弊端及其改进算法。提出了一种不仅可以增强对比度，还可以保留红外图像细节信息的新技术。在本文中将其称为自适应直方图小节修改。应用红外图像直方图的属性来自适应地确定分段位置。从上到下的方向计算灰度概率密度曲线的二阶微分系数。在红外图像的直方图中，将第一个拐点选择为高概率密度灰度级和低概率密度灰度级之间的分段点。然后分别映射和修改了低概率密度部分和高概率密度部分的直方图。最后，将子图像合并在一起，并重建输出的红外图像。在扩展红外图像中灰度级的动态范围时，对比度得到增强，原始灰度级几乎同时保留。同时，在灰度级之间保持适当的距离，以避免大的孤立颗粒被定义为图像中的斑点。通过几个红外图像来证明该方法的性能。实验结果表明，该方法大大提高了红外图像质量。此外，所提出的算法简单且易于执行。

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