An Infrared Target Detection Algorithm Based on Spatiotemporal Total Variation

To solve the false alarm problem caused by the complex background clutters in infrared small target detection, a novel detection method based on spatial-temporal total variation regularization is proposed. First, the input infrared image sequence is transformed into spatial-temporal infrared patch-tensor (STIPT) structure. This step can associate the spatial and temporal information by using the high dimensional data structures in the tensor domain. Then, weighted Schatten p-norm and spatial-temporal total variation regularization are incorporated to recover the low-rank background component for preserving the strong edges and corners, which can improve the accuracy of sparse target component recovery. Finally, the STIPT structure can be transformed into infrared image sequence by inverse operator, and an adaptive threshold segmentation is used to obtain the real target. Extensive experiments demonstrate that the proposed method can greatly improve the accuracy and efficiency of target detection with complex background clutters.