Abstract Top-Hat transformation is an essential technology in the field of infrared small target detection. Many modified Top-Hat transformation methods have been proposed based on the different structure of structural elements. However, these methods are still hard to handle the dim targets and complex background. It can be summarized as two reasons, one is that the structural elements cannot suppress the background adaptively due to the fixed value of structural elements in image. Another is that simple structural element cannot utilize the local feature for target enhancement. To overcome these two limitations, a special ring Top-Hat transformation based on M-estimator and local entropy is proposed in this paper. First, an adaptive ring structural element based on M-estimator is used to suppress the complex background. Second, a novel local entropy is proposed to weight structural element for capturing local feature and target enhancement. Finally, a comparison experiment based on massive infrared image data (more than 500 infrared target images) is done. And the results demonstrate that the proposed algorithm acquires better performance compared with some recent methods.

中文翻译：

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基于自适应 M-Estimator Ring Top-Hat 变换的红外小目标检测

摘要 Top-Hat变换是红外小目标检测领域的一项必不可少的技术。基于结构元素的不同结构，人们提出了许多改进的Top-Hat变换方法。然而，这些方法仍然难以处理暗淡的目标和复杂的背景。可以概括为两个原因，一是由于图像中结构元素的固定值，结构元素不能自适应地抑制背景。另一个是简单的结构元素不能利用局部特征进行目标增强。为了克服这两个限制，本文提出了一种基于 M-estimator 和局部熵的特殊环 Top-Hat 变换。首先，使用基于M-estimator的自适应环结构元素来抑制复杂背景。第二，提出了一种新的局部熵来加权结构元素以捕获局部特征和目标增强。最后进行了基于海量红外图像数据（500多幅红外目标图像）的对比实验。结果表明，与最近的一些方法相比，所提出的算法获得了更好的性能。