Recently, infrared small target detection problem has attracted substantial attention. Many works based on local low-rank model have been proven to be very successful for enhancing the discriminability during detection. However, these methods construct patches by traversing local images and ignore the correlations among different patches. Although the calculation is simplified, some texture information of the target is ignored, and targets of arbitrary forms cannot be accurately identified. In this paper, a novel target-aware method based on a non-local low-rank model and saliency filter regularization is proposed, with which the newly proposed detection framework can be tailored as a non-convex optimization problem, therein enabling joint target saliency learning in a lower dimensional discriminative manifold. More specifically, non-local patch construction is applied for the proposed target-aware low-rank model. By combining similar patches, we reconstruct them together to achieve a better generalization of non-local spatial sparsity constraints. Furthermore, to encourage target saliency learning, our proposed saliency filtering regularization term based on entropy is restricted to lie between the background and foreground. The regularization of the saliency filtering locally preserves the contexts from the target and surrounding areas and avoids the deviated approximation of the low-rank matrix. Finally, a unified optimization framework is proposed and solved with the alternative direction multiplier method (ADMM). Experimental evaluations of real infrared images demonstrate that the proposed method is more robust under different complex scenes compared with some state-of-the-art methods.

中文翻译：

---

TNLRS：用于红外小目标检测的具有显着性过滤正则化的目标感知非局部低秩建模


近年来，红外小目标检测问题引起了广泛关注。许多基于局部低秩模型的工作已被证明对于增强检测过程中的辨别力非常成功。然而，这些方法通过遍历局部图像来构造补丁，而忽略了不同补丁之间的相关性。虽然简化了计算，但忽略了目标的部分纹理信息，无法准确识别任意形态的目标。本文提出了一种基于非局部低秩模型和显着性滤波器正则化的新型目标感知方法，新提出的检测框架可以被定制为非凸优化问题，从而实现联合目标显着性在低维判别流形中学习。更具体地说，非局部补丁构建应用于所提出的目标感知低秩模型。通过组合相似的补丁，我们将它们重建在一起，以实现非局部空间稀疏约束的更好泛化。此外，为了鼓励目标显着性学习，我们提出的基于熵的显着性过滤正则化项被限制在背景和前景之间。显着性过滤的正则化局部保留了目标和周围区域的上下文，并避免了低秩矩阵的偏差近似。最后，提出了统一的优化框架，并使用替代方向乘子法（ADMM）进行求解。对真实红外图像的实验评估表明，与一些最先进的方法相比，所提出的方法在不同的复杂场景下更加鲁棒。