Infrared (IR) small target detection problem has attracted increasing attention. Tensor theory-based detection techniques have been widely utilized, while facing challenges such as tensor structures, background and target estimation. This paper proposes an IR dim and small target detection method based on 5-D spatial-temporal knowledge (5D-STD). Specifically, a 5-D whitened spatial-temporal patch-tensor is constructed. Then, we design a 5-D tensor nuclear norm for background estimation and a Moreau envelope-derived sparsity estimation norm. Furthermore, we establish a low-rank and sparse decomposition model with an alternating direction method of multipliers (ADMM)-based optimization scheme for IR target detection. Extensive experiments conducted on three real IR sequences prove the superiority of 5D-STD in terms of target detectability, background suppressibility and overall performance.
KEYWORDS: Target detection, Small targets, Infrared imaging, Infrared detectors, 3D modeling, 3D acquisition, Infrared radiation, Detection and tracking algorithms, Thermal modeling, 3D image processing
Infrared (IR) small target detection has been widely used in civilian and military applications. Although low-rank and sparse tensor decomposition theory has been widely employed, the estimations of target and background are still not precise enough. This paper proposes an IR small target detection method based on improved clustering and Bayesian guided-tracking regularization (STD-ICBT). Specifically, a 3-D spatial-temporal tensor is constructed first. Secondly, we improve the K-means clustering algorithm for lower-rank background fiber clusters and design an improved K-means clustering-based background estimation method, making it more accurate for background estimation. Furthermore, we design an efficient ADMM-based optimization algorithm for solving the target detection model. Compared with six state-of-the-art competitive methods, it demonstrates the superiority of STD-ICBT in terms of target detectability (TD), background suppressibility (BS), and overall performance
Recently, many state-of-the-art methods have been proposed for infrared (IR) dim and small target detection, but the performance of IR small target detection still faces with challenges in complicated environments. In this paper, we propose a novel IR small target detection method named local entropy characterization prior with multi-mode weighted tensor nuclear norm (LEC-MTNN) that combines local entropy characterization prior (LEC) and multi-mode weighted tensor nuclear norm (MTNN). First, we transform the original infrared image sequence into a nonoverlapping spatial-temporal patch-tensor to fully utilize the spatial and temporal information in image sequences. Second, a nonconvex surrogate of tensor rank called MTNN is proposed to approximate background tensor rank, which organically combines the sum of the Laplace function of all the singular values and multi-mode tensor extension of the construct tensor without destroying the inherent structural information in the spatial-temporal tensor. Third, we introduce a new sparse prior map named LEC via an image entropy characterization operator and structure tensor theory, and more effective target prior can be extracted. As a sparse weight, it is beneficial to further preserve the targets and suppress the background components simultaneously. To solve the proposed model, an efficient optimization scheme utilizing the alternating direction multiplier method (ADMM) is designed to retrieve the small targets from IR sequence. Comprehensive experiments on four IR sequences of complex scenes demonstrate that LEC-MTNN has the superior target detectability (TD) and background suppressibility (BS) performance compared with other five state-of-the-art detection methods.
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