Infrared Small Target Detection via Nonconvex Tensor Tucker Decomposition With Factor Prior

Infrared small target detection in complex scenes is an important but challenging research hotspot in infrared early warning fields. Previous studies have proved that low-rank Tucker decomposition (TD) achieves good detection performance in complex scenes. However, a key limitation of existing low-rank TD methods is that the rank needs to be set in advance, and an inaccurate predefined rank can lead to performance degradation. Inspired by the theorem that n-rank is upper bounded by the rank of each Tucker factor matrix, we propose a nonconvex tensor TD model with factor prior for infrared small target detection. In our method, we use a logdet-based function to constrain the latent factors of low-rank TD, which avoids empirical rank selection and sufficiently uses the latent data structure information in the factor matrix. Meanwhile, performing singular value decomposition (SVD) calculations on small factor matrices can reduce computational complexity. Then, group sparsity regularized total variation is used to better exploit the shared sparse pattern of difference images, which helps better remove background clutter and obtain better detection results. Finally, the proposed method is efficiently solved by the well-designed alternating direction method of multipliers (ADMM). Extensive experimental results demonstrate that our method is more effective and robust in complex scenes than other state-of-the-art methods.