Small target detection in infrared (IR) images has been widely applied for both military and civilian purposes. In this study, because IR images contain sparse and low-rank features in most scenarios, we propose an optimal IR patch-image (OIPI) model-based detection method to detect small targets in heavily cluttered IR images. First, the OIPI model was generated based on a conventional IR image model using a novel optimal patch size and sliding step adaptive selection algorithm. Secondly, the sparse and low-rank features of IR images were extracted and fused to generate an adaptive weighted parameter. Thirdly, the adaptive inexact augmented Lagrange multiplier (AIALM) algorithm was applied in the OIPI model to solve the robust principal component analysis (RPCA) optimization problem. Finally, an adaptive threshold method is proposed to segment and calibrate targets. Experimental results indicate that the proposed algorithm is capable of detecting small targets more stably and accurately, compared with state-of-the-art methods.

中文翻译：

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基于最优红外补丁图像模型的红外图像小目标检测解决改进的自适应RPCA问题

红外（IR）图像中的小目标检测已广泛应用于军事和民用目的。在本研究中，由于 IR 图像在大多数情况下都包含稀疏和低秩特征，我们提出了一种基于最优 IR 补丁图像 (OIPI) 模型的检测方法来检测重度杂乱 IR 图像中的小目标。首先，OIPI 模型是基于传统的 IR 图像模型生成的，该模型使用一种新颖的最佳补丁大小和滑动步长自适应选择算法。其次，提取并融合红外图像的稀疏和低秩特征，生成自适应加权参数。第三，在OIPI模型中应用自适应不精确增广拉格朗日乘子（AIALM）算法来解决鲁棒主成分分析（RPCA）优化问题。最后，提出了一种自适应阈值方法对目标进行分割和标定。实验结果表明，与最先进的方法相比，该算法能够更稳定、更准确地检测小目标。