Infrared small target detection (ISTD) faces two major challenges: a lack of discernible target texture and severe background clutter, which results in the background obscuring the target. To enhance targets and suppress backgrounds, we propose the Basis Decomposition Module (BDM) as an extensible and lightweight module based on basis decomposition, which decomposes a complex feature into several basis features and enhances certain information while eliminating redundancy. Extending BDM leads to a series of modules, including the Spatial Difference Decomposition Module (SD$^\mathrm{2}$M), Spatial Difference Decomposition Downsampling Module (SD$^\mathrm{3}$M), and Temporal Difference Decomposition Module (TD$^\mathrm{2}$M). Based on these modules, we develop the Spatial Difference Decomposition Network (SD$^\mathrm{2}$Net) for single-frame ISTD (SISTD) and the Spatiotemporal Difference Decomposition Network (STD$^\mathrm{2}$Net) for multi-frame ISTD (MISTD). SD$^\mathrm{2}$Net integrates SD$^\mathrm{2}$M and SD$^\mathrm{3}$M within an adapted U-shaped architecture. We employ TD$^\mathrm{2}$M to introduce motion information, which transforms SD$^\mathrm{2}$Net into STD$^\mathrm{2}$Net. Extensive experiments on SISTD and MISTD datasets demonstrate state-of-the-art (SOTA) performance. On the SISTD task, SD$^\mathrm{2}$Net performs well compared to most established networks. On the MISTD datasets, STD$^\mathrm{2}$Net achieves a mIoU of 87.68\%, outperforming SD$^\mathrm{2}$Net, which achieves a mIoU of 64.97\%. Our codes are available: https://github.com/greekinRoma/IRSTD_HC_Platform.

翻译：红外小目标检测面临两大挑战：目标缺乏可辨别的纹理特征以及严重的背景杂波，导致目标被背景淹没。为增强目标并抑制背景，我们提出了一种基于基分解的可扩展轻量级模块——基分解模块。该模块将复杂特征分解为若干基特征，在消除冗余的同时增强特定信息。扩展BDM得到了一系列模块，包括空间差异分解模块、空间差异分解下采样模块和时序差异分解模块。基于这些模块，我们开发了用于单帧红外小目标检测的空间差异分解网络，以及用于多帧红外小目标检测的时空差异分解网络。SD$^\mathrm{2}$Net在改进的U型架构中集成了SD$^\mathrm{2}$M和SD$^\mathrm{3}$M。我们利用TD$^\mathrm{2}$M引入运动信息，从而将SD$^\mathrm{2}$Net扩展为STD$^\mathrm{2}$Net。在SISTD和MISTD数据集上的大量实验证明了所提方法达到了最先进的性能。在SISTD任务上，SD$^\mathrm{2}$Net相较于多数现有网络表现优异。在MISTD数据集上，STD$^\mathrm{2}$Net取得了87.68%的mIoU，显著优于SD$^\mathrm{2}$Net的64.97%。我们的代码已开源：https://github.com/greekinRoma/IRSTD_HC_Platform。