Tiny and Dim Infrared Target Detection Based on Weighted Local Contrast

Robust detection of infrared (IR) tiny and dim targets in a single frame remains a hot and difficult problem in military fields. In this letter, we introduce a method for IR tiny and dim target detection based on a new weighted local contrast measure. Our method simultaneously exploits the local contrast of target, the consistency of image background, and the imaging characteristics of the background edges. The proposed method is simple to implement and computationally efficient. We compared our algorithm with six state-of-the-art methods on four real-world videos with different targets and backgrounds. Our method outperforms all the compared algorithms on the ground-truth evaluation with both higher detection rate and lower false alarm rate.