Fast and stable detection of dim and small infrared (IR) targets in complex backgrounds has important practical significance for IR search and tracking system. The existing small IR target detection methods usually fail or cause a high probability of false alarm in the highly heterogeneous and complex backgrounds. Continuous motion of a target relative to the background is important information regarding detection. In this article, a low-rank and sparse decomposition method based on greedy bilateral factorization is proposed for IR dim and small target detection. First, by analyzing the complex structure information of IR image sequences, the target is regarded as an independent sparse motion structure and an efficient optimization algorithm is designed. Second, the greedy bilateral factorization strategy is adopted to approximate the low-rank part of the algorithm, which significantly accelerates the efficiency of the algorithm. Extensive experiments demonstrate that the proposed method has better detection performance than the existing methods. The proposed method can still detect targets quickly and stably especially in complex scenes with weak signal-to-noise ratio.

中文翻译：

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基于图像序列贪婪双边分解的红外弱小目标检测

在复杂背景下快速稳定检测微弱的红外（IR）目标对于红外搜索跟踪系统具有重要的现实意义。现有的小红外目标检测方法在高度异质和复杂的背景下通常会失败或导致高概率的误报。目标相对于背景的连续运动是有关检测的重要信息。本文提出了一种基于贪婪双边分解的低秩稀疏分解方法，用于红外弱小目标检测。首先，通过分析红外图像序列的复杂结构信息，将目标视为一个独立的稀疏运动结构，设计了一种高效的优化算法。第二，采用贪心双边因式分解策略逼近算法的低秩部分，显着加快了算法的效率。大量实验表明，所提出的方法比现有方法具有更好的检测性能。特别是在信噪比较弱的复杂场景中，所提出的方法仍然可以快速稳定地检测目标。