Infrared Small Target Detection Method Based on Multidirectional Derivative and Local Contrast Difference

Robust infrared small target detection is of great essence for infrared search and track system. To detect the low signal-to-clutter ratio (SCR) target under the interference of high-intensity structural background, we propose an infrared small target detection method using multidirectional derivative and local contrast difference (MDLCD). Noting that infrared small target tends to have 2D Gaussian-like shape, we present a new multidirectional derivative model to reflect this distribution in each direction, which effectively enhances the target. Additionally, the adjacent background is applied to construct the local contrast difference model, whose role is to further suppress the high-intensity structural clutters. After this, the MDLCD map is obtained by weighting the above two filtered maps, along with an adaptive segmentation operation to finally extract the target. Experimental results verify that MDLCD achieves satisfactory performances in terms of SCR gain (SCRG) and background suppression factor (BSF).