Dim and Small Target Detection Based on Local Feature Prior and Tensor Train Nuclear Norm

When faced with complex scenes containing strong edge contours and noise, there are still more background residuals in the detection results of traditional algorithms, leading to a high false alarm rate. To solve the above problems, we propose an infrared dim and small target detection method that combines local feature prior and tensor train nuclear norm (TTNN). To suppress the strong edge contours, we first establish a background edge contour suppression function based on the structure tensor. Secondly, we propose a multi-frame density peak search algorithm to obtain local feature information by combining the features associated with multiple contiguous frames of the target. Then we use the local feature information and reweighting strategy to constrain the sparse components of the target signal, and describe the background low-rank components by the tensor train nuclear norm. Finally, we separate the target image from the background image using the alternating direction multiplier method. As compared with eight advanced algorithms, the method in this paper has a better background strong edge contour and a stronger noise suppression.