Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l₁ norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.

Infrared small target detection techniques remain a challenging task due to the complex background. To overcome this problem, by exploring context information, this research presents a data-driven approach called Attention-Guided Pyramid Context Network (AGPCNet). Specifically, we design Attention-Guided Context Block (AGCB) and perceive pixel correlations within and between patches at specific scales via Local Semantic Association (LSA) and Global Context Attention (GCA) respectively. Then the contextual information from multiple scales is fused by Context Pyramid Module (CPM) to achieve better feature representation. In the upsampling stage, we fuse the low and deep semantics through Asymmetric Fusion Module (AFM) to retain more information about small targets. The experimental results illustrate that AGPCNet has achieved state-of-the-art performance on three available infrared small target datasets. The source codes are available at https://github.com/Tianfang-Zhang/AGPCNet .

To improve the detection ability of infrared small targets in complex backgrounds, a novel method based on non-convex rank approximation minimization joint l_2,1 norm (NRAM) was proposed. Due to the defects of the nuclear norm and l₁ norm, the state-of-the-art infrared image-patch (IPI) model usually leaves background residuals in the target image. To fix this problem, a non-convex, tighter rank surrogate and weighted l₁ norm are instead utilized, which can suppress the background better while preserving the target efficiently. Considering that many state-of-the-art methods are still unable to fully suppress sparse strong edges, the structured l_2,1 norm was introduced to wipe out the strong residuals. Furthermore, with the help of exploiting the structured norm and tighter rank surrogate, the proposed model was more robust when facing various complex or blurry scenes. To solve this non-convex model, an efficient optimization algorithm based on alternating direction method of multipliers (ADMM) plus difference of convex (DC) programming was designed. Extensive experimental results illustrate that the proposed method not only shows superiority in background suppression and target enhancement, but also reduces the computational complexity compared with other baselines.

Infrared small target detection plays an important role in precision guidance, infrared warning, and other applications. The infrared patch-tensor (IPT) model has good detection performance, but some challenges still exist, such as the inaccurate representation of the background rank and poor robustness against noise and sparse interference. In order to solve these problems, a new IPT model is proposed in this article. First, to approximate the tensor rank more reasonably, t-SVD is generalized to multimodal t-SVD, and the tensor fibered rank is introduced. Moreover, the tensor fibered nuclear norm based on the Log operator (LogTFNN) is used to nonconvex approximate tensor fibered rank. Second, to suppress sparse interference such as strong edges and corner points, the prior information is extracted by the local structure tensor. Third, the hypertotal variation (HTV) is used as a joint regularization term to remove noise. Then, the alternating direction method of multipliers (ADMM) is used to solve the model. The proposed algorithm was tested on the 20 single-frame infrared images and six sequences of real scenes. Lots of experiments demonstrate that this algorithm has the robustness to noise and different scenes. Different evaluation metrics also show that the proposed algorithm has a significant superiority in detection performance compared with various state-of-the-art methods.

Infrared (IR) dim and small target detection in a highly complex background play an important role in many applications, and remain a challenging problem. In this paper, a novel method named stable multisubspace learning is presented to deal with this problem. The new method takes into account the inner structure of actual images so that it overcomes the shortage of the traditional method. First, by analyzing the multisubspace structure of heterogeneous background data, a corresponding image model is proposed using subspace learning strategy. This model is also stable to noise interference. Second, an efficient optimization algorithm is designed to solve the proposed IR image model. By adding the proper postprocessing procedure, we can get the detection result. Experiments on simulation scenes and real scenes show that the proposed method has superior detection ability under heterogeneous background.

The infrared search and track (IRST) system has been widely used, and the field of infrared small target detection has also received much attention. Based on this background, this paper proposes a novel infrared small target detection method based on non-convex optimization with Lp-norm constraint (NOLC). The NOLC method strengthens the sparse item constraint with Lp-norm while appropriately scaling the constraints on low-rank item, so the NP-hard problem is transformed into a non-convex optimization problem. First, the infrared image is converted into a patch image and is secondly solved by the alternating direction method of multipliers (ADMM). In this paper, an efficient solver is given by improving the convergence strategy. The experiment shows that NOLC can accurately detect the target and greatly suppress the background, and the advantages of the NOLC method in detection efficiency and computational efficiency are verified.