Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

TLDR

Infrared small target detection requires excellent performance, real‑time operation, and strong robustness, yet most state‑of‑the‑art methods achieve only one of these goals, making real‑time detection in complex scenes difficult. The paper proposes a robust infrared patch‑tensor model to simultaneously achieve real‑time performance, high detection accuracy, and robustness. The method employs a patch‑tensor model with a partial‑sum tensor nuclear norm and joint weighted l1 regularization, incorporates an improved local prior map, uses a reweighted sparsity scheme and efficient t‑SVD, and solves the resulting tensor robust PCA via ADMM. Experimental results demonstrate that the proposed approach outperforms existing state‑of‑the‑art methods.

Abstract

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.