Infrared Small Target Detection Based on Facet-Kernel Filtering Local Contrast Measure

Abstract

How to detect small targets accurately under complex background and low signal-to-clutter ratio is of great significance to the development of precision guided weapons and infrared early warning. The traditional local contrast method is difficult to detect small and dim targets in complex background. In this paper, in order to improve the traditional local contrast method and detect small targets effectively under complex background conditions, a novel method base on Facet-kernel filtering local contrast measure (FFLCM) is proposed for small target detection. Initially, a nest sliding window structure of the central layer and the surrounding background layer is given. Then, the Facet-kernel filter is used to enhance the target in the center layer, the gray similarity difference between the central layer and the surrounding layer is calculated to suppress the background. Finally, a threshold operation is used to extract target. Experimental results demonstrate that our proposed method could effectively enhance small targets and suppress complex background clutters simultaneously.