Infrared Small Target Detection in Image Sequences Based on Temporal Low-rank and Sparse Decomposition

In infrared small target detection tasks, targets usually occupy very few pixels and present as local bright spots, lacking prior knowledge such as shape and speed. In response to the above problems, a temporal low-rank and sparse decomposition and spatio-temporal continuity detection algorithm, names as TLRSD-STC, is proposed to detect small targets and eliminate false alarm targets. The proposed algorithm firstly expands the sequence images in time domain. The preliminary separation of small targets and background is achieved through low-rank and sparse decomposition, and target prediction maps can be obtained. Subsequently, targets and noise are further separated by an improved pipeline filter to obtain the final detection image. The proposed algorithm is validated on three sequence images containing complex scenes. Experimental results demonstrate that the algorithm has a higher detection rate and lower false alarm rate than other algorithms in complex scenes.