DWTFreqNet: Infrared Small Target Detection via Wavelet-Driven Frequency Matching and Saliency-Difference Optimization

In the field of infrared small target detection (IRSTD), targets generally exhibit dim characteristics and are difficult to distinguish from background clutter. Learning-based methods enhance feature representation through layer-by-layer propagation, but the sparse target information often diminishes. To address this, we propose DWTFreqNet, a network that splits input data to enhance both local saliency and global contextual differences. It incorporates complementary feature extraction modules designed to match the data distribution characteristics. Specifically, it first utilizes the discrete wavelet transform (DWT) to decompose the input data into low- and high-frequency components. For the low-frequency part, which carries key target information, we apply component-differential dense connections and DWT-based downsampling to maintain feature integrity. For the high-frequency part, rich in target–background contrast, an adaptive wavelet guidance mechanism (AWGM) optimizes multicomponent fusion via adaptive weighting, while a layer-wide discrepancy relationship capture (LDRC) module enhances target discrimination by linking multiscale feature maps. Comparative experiments on public datasets demonstrate its superiority over state-of-the-art (SOTA) methods. The code will be available at https://github.com/Kingwin97/DWTFreqNet