Hierarchical Structure Dependency Whitening for Single-Domain Generalized Infrared Small Target Detection

Existing infrared small target detection (IRSTD) methods typically assume that training and testing data share the same distribution. However, this assumption often fails in real-world applications due to environmental and sensor-induced variations, resulting in significant performance degradation caused by domain shifts. Besides, the inherently low signal-to-clutter ratio of targets in infrared images further impedes the extraction of underlying target information, increasing the risk of overfitting to domain-specific patterns. This severely constrains the generalizability of knowledge learned from source domains, particularly when training is confined to a single source domain due to the high cost of data annotation. To solve this problem, we propose hierarchical structure dependency whitening (HSDW) for single-domain generalized IRSTD. Specifically, we characterize domain discrepancies in infrared images as differences in structural information. Building upon this point, we employ feature whitening to mitigate the dependency on domain-specific structure information, whose distribution is diversely simulated by a dual-branch nonlinear transformation module. Moreover, we adopt a hierarchical suppression mechanism to alleviate the structural biases across multiple decoding stages, thereby facilitating more generalized target understanding across domains. Extensive experiments on three public IRSTD datasets demonstrate that our method achieves state-of-the-art performance.