Robust Infrared Small Target Detection Using a Novel Four-Leaf Model
Dali Zhou, Xiaodong Wang
Abstract
Infrared small target detection is widely used in the military field, and robust infrared small target detection has significant significance. Inspired by plants, an infrared small target detection method based on the four-leaf model is proposed. This model has both macro and micro attributes, with macro attributes referred to as the background suppressor (BS) and micro attributes referred to as the texture collector (TC). BS is a four-neighborhood model that can achieve background suppression while reducing the interference of bright background clutter in the target neighborhood to a certain extent. TC can collect texture information of small targets and improve the enhancement effect of small targets. The fusion of TC and BS can effectively suppress background clutter and improve the detection performance of infrared small targets. The experiment is carried out on five real infrared image sequences. The results show that the proposed infrared small target detection method can improve the detection rate and reduce the false alarm rate in the face of infrared images with complex backgrounds. Compared to existing algorithms, the algorithm has high robustness.