Infrared Small Target Detection Based on Multidirectional Cumulative Measure

Robustness of small target detection is a researchable hotspot in infrared (IR) surveillance system. The residual phenomenon of background clutter is universal in current local comparison methods. The algorithm of sparse low-rank decomposition restoration cannot be applied to the actual situations due to the long time consumption. This letter proposes a multi-directional cumulative measure (MDCM) to enhance the saliency and effectiveness of weak-small target detection. First, multi-directional cumulative mean difference is implemented in central layer and background layer to estimate the background, while multi-directional cumulative derivative multiplying (MDCDM) is calculated in central-active layer to characterize the overall target's heterogeneity, and then the technology of image fusion is adopted to eliminate the interference of false target. Finally, a simple adjudicative technology is employed toward separated target region from complex scenes. Compared to up-to-date existing approaches, extensive simulational testing on four public datasets prove that the proposed approach is capable of separating small targets efficiently from an irregular background in a single-scale window and achieving a comparable or even better accuracy.