Infrared Dim and Small Target Detection Based on Greedy Bilateral Factorization in Image Sequences

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.