Infrared Small Target Detection Based on Monogenic Signal Decomposition

Robust detection of infrared small targets under complex backgrounds is of great significance for infrared search and tracking applications. However, the inherent problem of limited prior features for infrared small targets has always made its detection task a challenging research topic. In order to solve the problem, we propose a novel infrared small target detection (IRSTD) method based on monogenic signal decomposition and feature expansion, which can effectively enrich and extract the potential features of the target. First, a series of local information of the original image is obtained through the monogenic signal constructed by the Riesz transform. Then, various features of the small target are extracted from different local signals, including the direction feature, edge feature, and local saliency feature. Finally, the fusion of target features is completed through signal reconstruction, thereby achieving target detection. This method not only pays attention to the local salient characteristic of the target but also supplements the consideration of other characteristics of the target, providing a new idea for small target detection. The experimental results on real infrared images show that the proposed method framework is reasonable and effective, and possesses better detection performance and good generalization compared to other state-of-the-art methods.