Infrared Small Target Detection Based on Local Contrast Measure With a Flexible Window

Infrared (IR) small target detection is crucial for IR search and tracking systems. Saliency-based detection techniques have gradually demonstrated their superiority. The existing methods often adopt a fixed rectangular window for sliding window filtering to measure the saliency of suspected targets, making it difficult to accommodate targets of different sizes and easily leading to contamination by nontarget pixels. Therefore, the rectangular sampling window approach increases the difficulty of detecting IR small targets. In this letter, a novel flexible window local contrast measure (FWLCM) is proposed. First, a flexible window estimation method is suggested that dynamically adjusts the shape of the saliency measurement window based on the heterogeneous variations between the target and the background by performing a flexible region growing. Especially, the local contrast is calculated at an adaptive scale to enhance the target saliency. Second, a directional sector local contrast calculation method is introduced to optimize the statistical way of background region characteristics, reducing the interference of discrete noise in the background statistical features. Finally, adaptive threshold segmentation is utilized to extract the real target. Compared with the existing methods, experimental results on four real datasets demonstrate the superiority of FWLCM in complex backgrounds.