Infrared Small Target Detection (IRSTD) aims to segment small targets from infrared clutter background. Existing methods mainly focus on discriminative approaches, i.e., a pixel-level front-background binary segmentation. Since infrared small targets are small and low signal-to-clutter ratio, empirical risk has few disturbances when a certain false alarm and missed detection exist, which seriously affect the further improvement of such methods. Motivated by the dense prediction generative methods, in this paper, we propose a diffusion model framework for Infrared Small Target Detection which compensates pixel-level discriminant with mask posterior distribution modeling. Furthermore, we design a Low-frequency Isolation in the wavelet domain to suppress the interference of intrinsic infrared noise on the diffusion noise estimation. This transition from the discriminative paradigm to generative one enables us to bypass the target-level insensitivity. Experiments show that the proposed method achieves competitive performance gains over state-of-the-art methods on NUAA-SIRST, IRSTD-1k, and NUDT-SIRST datasets. Code are available at https://github.com/Li-Haoqing/IRSTD-Diff.

翻译：红外小目标检测（IRSTD）旨在从红外杂波背景中分割出小目标。现有方法主要侧重于判别式方法，即像素级的前景-背景二值分割。由于红外小目标体积小且信杂比低，当存在一定虚警和漏检时，经验风险几乎不受干扰，这严重制约了此类方法的进一步改进。受密集预测生成方法的启发，本文提出一种用于红外小目标检测的扩散模型框架，通过掩模后验分布建模补偿像素级判别。此外，我们在小波域设计了一个低频隔离模块，以抑制红外固有噪声对扩散噪声估计的干扰。从判别范式到生成范式的转变使我们能够绕过目标级的不敏感性。实验表明，所提方法在NUAA-SIRST、IRSTD-1k和NUDT-SIRST数据集上取得了优于现有最先进方法的性能提升。代码见https://github.com/Li-Haoqing/IRSTD-Diff。