Multidirectional Diffusion Difference Measure Approach for IR Small Target Detection

The accurate detection of small infrared (IR) targets in complex backgrounds is crucial for the effective operation of IR search and track (IRST) systems. In this letter, we propose a novel multidirectional diffusion difference measure (MDDDM) approach for accurate and low-complexity IR small target detection. MDDDM consists of three stages. First, a gray intensity measure (GIM) is generated to highlight the important features and suppress high-intensity background regions by using a computationally efficient difference of Gaussian (DoG) filter. Second, the diffusion difference measure (DDM) kernel is computed by fusing an improved Gaussian kernel (IGK) and a directional filter kernel (DFK), utilizing feature minimization instead of omnidirectional gradient judgment to suppress background edge clutters. Finally, threshold segmentation operation is used for precise localization and extraction of the target from the background. Experimental results demonstrate that MDDDM outperforms other state-of-the-art algorithms in terms of detection rate and false alarm rate while maintaining the fastest running speed.