Infrared Small Target Detection Based on Multidirectional Gradient

Infrared (IR) small target detection has always been a challenge in IR search and track systems, which require excellent detection of ultraweak target, robustness, and real-time speed. To achieve these requirements, this letter proposes a new small target detection method that can remarkably suppress the background clutters and sharply enhance the weak target. First, the method locally models the image with facet model and multidirectionally derives the model to separate the target from background clutters; furthermore, via filtering, the multidirectional derivative subbands are obtained. Then, the candidate target points are screened based on the assumption that IR small targets are isotropic. Third, for candidate target points, all the derivative subbands are fused to construct the contrast measure map. Finally, the small target is extracted by threshold segmentation. Experimental results demonstrate that the proposed method can suppress the background better than several state-of-the-art methods while enhancing the target in real time.