Facet Derivative-Based Multidirectional Edge Awareness and Spatial-Temporal Tensor Model for Infrared Small Target Detection

Infrared (IR) small target detection in the complex background is an important but challenging research hotspot in the field of target detection. The existing methods usually cause high false alarms in the complex background and fail to make full use of the complete information of the image. In this article, a novel IR small target detection model that combines facet derivative-based multidirectional edge awareness with spatial-temporal tensor (FDMDEA-STT) is presented. First, we construct an STT model (STTM) to transform the target detection problem into a low-rank and sparse tensor optimization problem based on the prior information of the target and background in the spatial-temporal domain. Then, based on the facet derivative, we define a multidirectional edge awareness mapping and fuse it into the STTM as sparse prior information. Finally, an effective algorithm based on the alternating direction method of multipliers (ADMM) is designed to solve the above model. The effectiveness of the proposed method is verified on eight real IR image sequences. Experimental results demonstrate that the proposed method has better detection performance than the existing state-of-the-art methods.

Automated summary

In this paper, a novel IR small target detection model is proposed, which effectively addresses the issue of high false alarms in complex backgrounds by combining multidirectional edge awareness and spatial-temporal tensor. Experimental results demonstrate that the proposed method has better detection performance than the existing state-of-the-art methods on real IR image sequences.