Robust Infrared Small Target Detection Using Multiscale Gray and Variance Difference Measures

As a long-standing problem, infrared small target detection is challenging due to the dimness of targets and the complexity of background. Considering the limitation of traditional approaches, we propose an accurate and robust method for infrared small target detection using multiscale gray and variance difference measures. A multiscale adaptive gray difference measure is first used to enhance small targets and improve detection accuracy. Then, a multiscale variance difference measure is proposed to alleviate the impact of background fluctuation and improve the robustness of our method. By integrating these two approaches, targets can be extracted accurately using a threshold-adaptive segmentation. Extensive experiments have been conducted on datasets with various scenes. Results have demonstrated the effectiveness and outperformance of our method as compared to the state-of-the-art methods.