Infrared small target detection is a crucial stage in many searching and tracking applications. Many tensor decomposition-based methods have achieved well performance in the scenes with uniform backgrounds and salient targets. However, the performance is potentially prone to be degraded when encountering highly complex scenes. It is mainly because the decomposition error caused by the sparse edge structures and imprecise tensor rank measures make the recovered background deviate from the real one. To mitigate these issues, we introduce an infrared sequence tensor decomposition-based method, which combines an intrinsic tensor rank measure (ITRM) and spatial structure prior to improve background recovery. With the inter-frame background correlation, ITRM is used to regularize the low-rank component of the observed tensor, which encodes the intrinsic tensor rank insights delivered by two most typical tensor decompositions to finely rectify the estimation bias of the tensor rank. To further suppress edge structures during the decomposition, we design a spatial descriptor with edge awareness by using a nonlocal structure tensor to delineate intra-frame structural edge. Furthermore, an adaptive indicator, which fuses spatial edge information and sparsity enhancing weight, is employed to replace the inflexibly and globally sparse penalty. The solution of the proposed model is addressed by an alternative direction minimization of multipliers (ADMM). Extensive experiments on real-world infrared sequences demonstrate the outperformance of the proposed method against other state-of-the-art ones, both quantitatively and qualitatively.

红外小目标检测是许多搜索和跟踪应用程序中的关键阶段。许多基于张量分解的方法在具有均匀背景和显着目标的场景中均取得了良好的性能。但是，遇到高度复杂的场景时，性能可能会降低。这主要是由于稀疏的边缘结构和不精确的张量等级措施引起的分解误差使恢复的背景偏离了真实背景。为了缓解这些问题，我们引入了一种基于红外序列张量分解的方法，该方法结合了固有张量秩度量（ITRM）和空间结构，从而提高了背景恢复率。借助帧间背景相关性，ITRM用于规范所观察到的张量的低秩分量，它编码由两个最典型的张量分解提供的内在张量等级见解，以精细地校正张量等级的估计偏差。为了在分解过程中进一步抑制边缘结构，我们通过使用非局部结构张量来描绘帧内结构边缘来设计具有边缘意识的空间描述符。此外，融合了空间边缘信息和稀疏性增强权重的自适应指示器被用来代替僵化和全局稀疏的惩罚。提出的模型的解决方案通过乘数的替代方向最小化（ADMM）解决。在现实世界中的红外序列上进行的大量实验证明，该方法在定量和定性方面均优于其他最新技术。