Toward Robust Infrared Small Target Detection via Frequency and Spatial Feature Fusion

Infrared small target detection (IRSTD) faces significant challenges due to the small scale and low intensity of targets, which are characterized by extremely sparse features. Most existing methods primarily concentrate on spatial features while neglecting the significant cluttered interference inherent in complex backgrounds. Such an oversight poses substantial challenges in distinguishing targets from background noise, thereby limiting detection performance. Drawing inspiration from the frequency characteristics that differentiate targets from backgrounds in infrared images, we introduce an innovative detection network that leverages high- and low-frequency partitioning and interaction. Specifically, we introduce a patch-wise fast Fourier transform (PFFT), which divides the input image into patches and applies the Fourier transform to each patch. Subsequently, we employ convolutional neural networks (CNNs) for learnable high- and low-frequency partitioning and propose a learnable frequency augmentation module (FAM) to enhance the interfrequency and intrafrequency feature. This methodology effectively harnesses the spatial information inherent in both high and low frequencies to suppress background clutter and accurately extract sparse target features. Furthermore, to further integrate frequency information with spatial information, we propose a frequency spatial fusion module (FSFM) to merge features from frequency and spatial domains. Experimental results show that our method surpasses state-of-the-art techniques on four publicly available datasets.