Spatial–Frequency Domain Transformation for Infrared Small Target Detection

With the development of infrared technology, infrared small target detection (IRSTD) is widely applied in fields such as environmental monitoring, marine rescue, and forest fire prevention. Existing IRSTD methods are often based on spatial domain approaches, which preserve target features in the spatial domain but overlook the characteristics of infrared small targets in the frequency domain. In frequency domain methods, infrared small targets are typically considered as high-frequency components, while the continuous background is regarded as low-frequency components. However, infrared small targets often have complex backgrounds, strong edges, and noise generated during imaging, all of which are also reflected as high-frequency components, leading to false detections. To overcome this issue and fully explore the potential of IRSTD in the frequency domain, we propose a novel network, SFDTNet, which integrates frequency-domain attention and U-Structure for IRSTD. In the encoding phase, spatial feature extraction is applied to the infrared small target. In the decoding stage, global-scale spatial features are modeled in the frequency domain to achieve more precise reconstruction of small targets while reducing the interference of background high-frequency clutter. Frequency domain self-attention (FDSA) introduces an attention mechanism to model global information in the frequency domain and capture the importance of different frequency components. Adaptive frequency selection network (AFSN) incorporates learnable masks to adaptively modulate high- and low-frequency components in the frequency domain. Finally, a deep supervision strategy is employed to help the network learn features more effectively. Experimental results demonstrate that it effectively retains the shape and contours of small targets while achieving a very low false detection rate. Compared with existing state-of-the-art methods, our approach shows superior performance and better robustness.