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：

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TNLRS：具有显着性过滤正则化的目标感知非本地低秩建模，用于红外小目标检测

近来，红外小目标检测问题引起了广泛的关注。事实证明，许多基于局部低秩模型的作品在增强检测过程中的可分辨性方面都非常成功。但是，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。尽管简化了计算，但是忽略了目标的某些纹理信息，并且无法准确识别任意形式的目标。本文提出了一种基于非局部低秩模型和显着性滤波正则化的目标感知新方法，可以将新提出的检测框架调整为非凸优化问题，从而实现联合目标显着性在低维判别流形中学习。进一步来说，非本地补丁构造应用于建议的目标感知低等级模型。通过组合相似的补丁，我们将它们重建在一起以更好地概括非局部空间稀疏性约束。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区域和周围区域的上下文，并避免了低秩矩阵的近似偏差。最后，提出了一个统一的优化框架，并用替代方向乘子法（ADMM）进行了求解。