A Comprehensive Benchmark for Spatiotemporal Tensor-Based Infrared Small Target Detection

Optimization-based methods have gained prominence in infrared small target detection (ISTD) due to their effectiveness in handling sparse targets. However, existing approaches often struggle with spatio-temporal modeling, resulting in suboptimal background suppression and target misclassification. Moreover, stringent recovery tolerances limit computational efficiency in sequential infrared processing. To overcome these challenges, we introduce a novel approach that enhances both background estimation and target detection by leveraging spatio-temporal features. Specifically, we integrate a four-dimensional (4-D) spatio-temporal tensor with a fully connected tensor network (FCTN) completion strategy, utilizing the sum of nuclear norms to maximize intermode correlations while minimizing clutter. For target extraction, we employ a dual Gaussian-core spatial saliency filter (SSF) and a temporal difference filter (TDF), enabling precise discrimination between true targets and static interference. The resulting model—Spatial-Temporal Prior-Assisted Fully Connected Tensor Network (STPA-FCTN)—balances background suppression with target preservation. Furthermore, we systematically analyze recovery convergence in STPA-FCTN and identify an optimal tolerance of 10-3using the proposed open-source TensorISTD benchmark. Extensive evaluations on public datasets confirm the superior numerical and visual performance of our method over state-of-the-art techniques. Code and benchmark resources are available at https://github.com/fengyiwu98/TensorISTD.