Robust Infrared Small Target Detection via Multidirectional Derivative-Based Weighted Contrast Measure

Infrared (IR) small target detection in complex backgrounds is one of the key technologies in IR search and tracking applications. Although significant progress has been made over the past few decades, how to separate a small target from complex backgrounds remains a challenging task. In this letter, a novel small target detection method via multidirectional derivative-based weighted contrast measure (MDWCM) is proposed. Initially, multidirectional derivative subbands are quickly obtained by the facet model. Then, an effective division scheme of surrounding area is performed to capture the derivative properties of the target. A new local contrast measure is constructed to simultaneously enhance the target and suppress the background clutter. Third, the MDWCM maps constructed from all derivative subbands are integrated to enhance the robustness of detection. Finally, the small target is extracted by an adaptive segmentation method. The experimental results demonstrate that the proposed algorithm performs favorably compared to other state-of-the-art methods.

Automated summary

A novel small target detection method based on multidirectional derivatives is proposed, which can effectively separate targets from backgrounds. The method constructs a local contrast measure, integrates MDWCM maps from all derivative subbands to enhance detection robustness, and ultimately achieves adaptive segmentation and extraction of small targets.