Infrared small target detection based on Gaussian curvature filtering and partial sum of singular values

To reduce noise and background distractions, we propose a detection algorithm based of Gaussian curvature filtering (GCF) and partial sum of singular values (PSSV). Above all, aiming at the false alarms caused by noise, using the prior knowledge of natural image have the characteristics of approximate developable, GCF utilizes the variation model to obtain an approximately noise-free image. Secondly, we adopt PSSV to suppress the background by pointing out that the reason why Robust PCA is inaccurate in the background estimation is that the hypothesis at the edge of the background does not match the reality, and we present a model solving algorithm on the ground of imprecise augmented Lagrangian multiplier method (IALM). At last, we use an adaptive threshold segmentation algorithm to segment the target. The model has better noise clutter suppression and detection accuracy than other representative methods.