Infrared Small Target Detection via Spatial-Temporal Total Variation Regularization and Weighted Tensor Nuclear Norm

Abstract

The infrared small and dim targets are often buried in strong clutters and noise, which requires robust and efficient detection approaches to achieve search and track task. In this paper, a novel infrared small target detection approach based on tensor robust principal component analysis (RPCA) is proposed by fully utilizing both spatial and temporal information. Traditional total variation (TV) regularizationbased method only considers the spatial information of a single frame, and its efficiency cannot meet the real-time processing requirement. We incorporate an anisotropic spatial-temporal TV regularization to utilize both single-frame and inter-frame information. First, the input image sequence is transformed into an infrared patch tensor (IPT) model. Then, the spatial-temporal TV regularization and weighted IPT model (STTVWNIPT) is proposed to separate the target and background. For the low-rank background component, we adopt the weighted tensor nuclear norm and spatial-temporal TV regularization to describe the smoothness. For sparse target component and a noise component, we use the l 1 norm and Frobenius norm terms to characterize, respectively. Finally, the proposed model can be solved efficiently by the alternating direction method of multipliers (ADMM). The extensive experiments demonstrate that the proposed model outperforms the other competitive methods, and it is much more efficient than traditional TV-based method.INDEX TERMS Infrared patch-tensor model, small target detection, weighted tensor robust principal component analysis, total variation (TV).

A Method for Infrared Sea-Sky Condition Judgment and Search System: Robust Target Detection via PLS and CEDoG

“…Local Contrast Method [17]- [20] Spatio-temporal Saliency Approach [21]- [22] Low-rank Tensor Completion [23]- [24] Based on Deep Learning [25]- [29] It can be known from above scientific research situation that these current methods are more suitable for targets with a single feature in the entire image [30][31][32][33][34]. On the other word, sea-sky background clutter and wave noise in the space or transform domain are irrelevant to the target [35][36][37][38][39][40][41].…”

Infrared small target detection based on non‐convex triple tensor factorisation

“…In addition to this, RIPT model [17] treats singular values equally, so, less weight should be allocated to the larger singular values. Also to lower down the burden of SVD computation, and to approximate the low rank background tensor patch properly, tensor nuclear norm (TNN) [23] has been applied recently in many infrared small target detection approaches [18,24,25,19,20]. Hence, our first motivation is to propose a method which could address the low-rank background tensor approximation, as well as, the SVD computational cost issue.…”

Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection

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