Effective and robust infrared small target detection with the fusion of polydirectional first order derivative images under facet model

• The amendatory first order directional derivative based on facet model is employed to enhance infrared small targets.
• Eight amendatory first-order directional derivative images in pre-set directions are fused together.
• Principal component analysis is employed to enhance the targets in the fusion image.

The robust detection of IR small target acts as one of the key techniques in the infrared search and tracking system (IRSTS). This paper presents a new method of small-target detection which formulates the problem as the detection of Gaussian-like spot. Initially, the amendatory first-order directional derivative (AFODD) based on facet model is applied to get the polydirectional derivative IR images, and the direction information of targets is reserved in these images. Then, the AFODD images are fused together to ensure the robustness and effectiveness of target detection. At last, the Principal Component Analysis (PCA) method is carried out to make targets in the fusion image more prominent, so that they can be extracted out by a simple threshold segmentation. Experiment results show that the presented method performs well even in the IR images with complex backgrounds.