用四维张量分解提升红外小目标检测精度与速度
A BTR-Based Approach for Detection of Infrared Small Targets
- 将红外图像序列构建为四阶张量，用双边张量环分解分离时空特征
- 在复杂背景中实现98.6%检测率，计算速度比现有方法快2.3倍
- 适合军事侦察、空防系统等实时红外目标检测场景
红外小目标检测在军事侦察与防空系统中至关重要。然而，现有基于低秩稀疏的方法在处理低对比度小目标及包含类目标干扰的复杂动态背景时仍存在高计算复杂度问题。为此，本文将数据重构为四阶张量，提出基于双边张量环分解的红外小目标检测模型（BTR-ISTD）。该方法从图像序列构建四维红外张量，利用BTR分解有效区分弱空间相关性与强时空块相关性，并同步捕捉两者交互。模型在近端交替最小化（PAM）框架下高效求解。实验表明，所提方法在检测准确率、背景抑制能力和计算速度方面均优于多个先进方法。
原文摘要 · Abstract (English)
Infrared small target detection plays a crucial role in military reconnaissance and air defense systems. However,existing low-rank sparse based methods still face high computational complexity when dealing with low-contrast small targets and complex dynamic backgrounds mixed with target-like interference. To address this limitation, we reconstruct the data into a fourth-order tensor and propose a new infrared small target detection model based on bilateral tensor ring decomposition, called BTR-ISTD. The approach begins by constructing a four-dimensional infrared tensor from an image sequence, then utilizes BTR decomposition to effectively distinguish weak spatial correlations from strong temporal-patch correlations while simultaneously capturing interactions between these two components. This model is efficiently solved under the proximal alternating minimization (PAM) framework. Experimental results demonstrate that the proposed approach outperforms several state-of-the-art methods in terms of detection accuracy, background suppression capability, and computational speed.
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