Infrared small target detection in a complex background has always been a challenging task in an infrared detection system. The existing methods based on the infrared patch image (IPI) model have achieved a good result but are sensitive to the complex background. So, to effectively detect the small target in complex background, model based on the reweighted IPI model along with total variance (TV) is proposed in this study. In this study firstly, the problem of using nuclear norm minimisation (NNM) in the existing IPI-based methods is discussed, and a solution is proposed by replacing the existing NNM with the ℒp- norm minimisation of singular values in the existing IPI methods. Secondly, a TV regularisation term is added to the background patch image to suppress the noise and preserve the strong edges in the background. The proposed method is solved by the alternating direction method of the multiplier. The robustness of the proposed method is validated by experimenting with the large dataset of real infrared images as well as the synthetic images. The proposed method not only has good background suppression ability, but also enhances and detect the target well in comparisons with the other baseline methods.

中文翻译：

---

基于非凸的小目标检测加权红外斑块图像模型 ℒp-规范最小化和电视正则化

复杂背景下的红外小目标检测一直是红外检测系统中的一项艰巨任务。现有的基于红外斑块图像（IPI）模型的方法取得了很好的效果，但对复杂的背景敏感。因此，为有效地检测复杂背景下的小目标，本文提出了基于重加权IPI模型和总方差（TV）的模型。在本研究中，首先，讨论了在现有的基于IPI的方法中使用核规范最小化（NNM）的问题，并提出了一种解决方案，即用NIP替换现有的NNM。ℒp- 在现有IPI方法中规范最小化奇异值。其次，将电视正规化项添加到背景色块图像中，以抑制噪声并保留背景中的强边缘。所提出的方法是通过乘法器的交替方向法解决的。通过对真实红外图像以及合成图像的大型数据集进行实验，验证了该方法的鲁棒性。与其他基线方法相比，该方法不仅具有良好的背景抑制能力，而且可以很好地增强和检测目标。