Fast and Robust Small Infrared Target Detection Using Absolute Directional Mean Difference Algorithm
This work addresses the problem of detecting small targets in infrared images for IRST systems, but it is incremental as it builds on existing concepts like AAGD.
The paper tackled infrared small target detection in cluttered backgrounds by proposing a directional algorithm called absolute directional mean difference (ADMD), which effectively enhances targets and suppresses structural clutter, as proven by simulation results on real infrared images.
Infrared small target detection in an infrared search and track (IRST) system is a challenging task. This situation becomes more complicated when high gray-intensity structural backgrounds appear in the field of view (FoV) of the infrared seeker. While the majority of the infrared small target detection algorithms neglect directional information, in this paper, a directional approach is presented to suppress structural backgrounds and develop a more effective detection algorithm. To this end, a similar concept to the average absolute gray difference (AAGD) is utilized to construct a novel directional small target detection algorithm called absolute directional mean difference (ADMD). Also, an efficient implementation procedure is presented for the proposed algorithm. The proposed algorithm effectively enhances the target area and eliminates background clutter. Simulation results on real infrared images prove the significant effectiveness of the proposed algorithm.