  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.
