HDNet: A Hybrid Domain Network With Multiscale High-Frequency Information Enhancement for Infrared Small-Target Detection

The infrared small-target detection (IRSTD) task involves identifying and separating small targets from complex backgrounds. However, these targets pose significant challenges due to their small, variable sizes and dim appearance with a low signal-to-noise ratio, often obscured by cluttered backgrounds. Standard spatial-domain convolutional neural networks (CNNs) act as low-pass filters, hindering their ability to detect small, variably sized, low-contrast targets against complex backgrounds. Infrared images (IRIs) also exhibit diverse spectral energy distributions, yet CNNs lack a global spectral view to discern these patterns, making them susceptible to background clutter. To address these shortcomings, we propose a novel hybrid-domain network (HDNet), which fuses frequency-domain features with conventional spatial-domain CNN features to markedly enhance target-background contrast and explicitly suppress background interference. Specifically, the HDNet comprises two main branches: the spatial-domain branch and the frequency-domain branch. In the spatial domain, we innovatively introduce a multiscale atrous contrast (MAC) convolution module, utilizing multiple parallel atrous contrast convolutions (ACCs) with varying kernel sizes to enhance the perception of small, variably sized targets. In the frequency domain, we have specifically designed the dynamic high-pass filter (DHPF) module, hierarchically calculating low-frequency signal energy and dynamically removing specific low-frequency information to preserve high-frequency image details. This effectively filters out slowly varying low-frequency backgrounds, highlighting small targets. Comprehensive ablation studies and experimental analysis on three datasets (IRSTD-1K, NUAA-SIRST, and NUDT-SIRST) validate the HDNet’s effectiveness and superiority compared to 26 state-of-the-art (SOTA) methods.