Neural spatial-temporal tensor representation for infrared small target detection

Fengyi Wu, Simin Liu, Haoan Wang, Bingjie Tao, Junhai Luo, Zhenming Peng

Abstract: Optimization-based approaches dominate infrared small target detection as they leverage infrared imagery’s intrinsic low-rankness and sparsity. While effective for single-frame images, they struggle with dynamic changes in multi-frame scenarios as traditional spatial–temporal representations often fail to adapt. To address these challenges, we introduce a Neural-represented Spatial–Temporal Tensor (NeurSTT) model. This framework employs nonlinear networks to enhance spatial–temporal feature correlations in background approximation, thereby supporting target detection in an unsupervised manner. Specifically, we employ neural layers to approximate sequential backgrounds within a low-rank informed deep scheme. A neural three-dimensional total variation is developed to refine background smoothness while reducing static target-like clusters in sequences. Traditional sparsity constraints are incorporated into the loss functions to preserve potential targets. By replacing complex solvers with a deep updating strategy, NeurSTT simplifies the optimization process in a domain-awareness way. Visual and numerical results across various datasets demonstrate that our method outperforms detection challenges. Notably, it has 16.6× fewer parameters and averaged 22.44% higher in IoU compared to the suboptimal method on 256 × 256 sequences. The code is available at https://github.com/fengyiwu98/NeurSTT.

External IDs:dblp:journals/pr/WuLWTLP26