Detecting the small targets from a heterogeneous background in an infrared image is a challenging problem, which has received extensive attention. In this article, we propose a method in terms of tensor ring (TR) decomposition and nonlinear multiscale morphological top-hat transformation for infrared small target detection (ISTD). First, a tensor model with prior knowledge is constructed for extracting the structural features of multiple infrared images. Then, the problem of small target detection is converted into a problem of minimizing the tensor rank with TR. Based on the TR decomposition model, we introduce the top-hat regularization into our model with multiple structural elements of different size to perform morphological operations. The corresponding morphological model exploits a more accurate ring top-hat regularization expression through adaptive nonlinear combination for the ISTD problem. Finally, the optimization of the model is realized by the closed solution given by the alternating direction method of multipliers algorithm. In order to verify the superior performance of our method, our method is compared with a number of advanced detection models. By analyzing the results of comparison experiments, the detection accuracy and precision of our model in the detection of small infrared targets have been improved. Even in complex background conditions, our model also maintain a good robustness.

中文翻译：

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当红外小目标检测遇到张量环分解：多尺度形态学框架

从红外图像中的异质背景中检测小目标是一个具有挑战性的问题，已受到广泛关注。在本文中，我们提出了一种基于张量环 (TR) 分解和非线性多尺度形态顶帽变换的红外小目标检测 (ISTD) 方法。首先，构建具有先验知识的张量模型，用于提取多幅红外图像的结构特征。然后，将小目标检测的问题转化为用TR最小化张量秩的问题。基于TR分解模型，我们将顶帽正则化引入我们的模型中，具有多个不同大小的结构元素来执行形态学操作。相应的形态模型通过自适应非线性组合针对 ISTD 问题利用了更准确的圆顶正则化表达式。最后通过乘法器交替方向法给出的闭解来实现模型的优化。为了验证我们的方法的优越性能，我们的方法与一些先进的检测模型进行了比较。通过对比实验结果的分析，提高了我们的模型在红外小目标检测中的检测精度和精度。即使在复杂的背景条件下，我们的模型也保持了良好的鲁棒性。模型的优化是通过乘子算法的交替方向法给出的闭解来实现的。为了验证我们的方法的优越性能，我们的方法与一些先进的检测模型进行了比较。通过对比实验结果的分析，提高了我们的模型在红外小目标检测中的检测精度和精度。即使在复杂的背景条件下，我们的模型也保持了良好的鲁棒性。模型的优化是通过乘子算法的交替方向法给出的闭解来实现的。为了验证我们的方法的优越性能，我们的方法与一些先进的检测模型进行了比较。通过对比实验结果的分析，提高了我们的模型在红外小目标检测中的检测精度和精度。即使在复杂的背景条件下，我们的模型也保持了良好的鲁棒性。