Infrared Small Target Detection via Local-Global Feature Fusion

Abstract

Due to the high-luminance (HL) background clutter in infrared (IR) images, the existing IR small target detection methods struggle to achieve a good balance between efficiency and performance. Addressing the issue of HL clutter, which is difficult to suppress, leading to a high false alarm rate, this letter proposes an IR small target detection method based on local-global feature fusion (LGFF). We develop a fast and efficient local feature extraction operator and utilize global rarity to characterize the global feature of small targets, effectively suppressing a significant amount of HL clutter. By integrating local and global features, we achieve further enhancement of the targets and robust suppression of the clutter. Experimental results demonstrate that the proposed method outperforms existing methods in terms of target enhancement, clutter removal, and real-time performance.