FDFE-Net: Frequency Domain Feature Enhancement Network for Infrared Small Target Detection

Abstract

Infrared small target detection IRSTD encounters challenges due to the tiny sizes of targets and interference from complex backgrounds. To overcome these issues, this paper proposes a novel Frequency Domain Feature Enhancement Network FDFE-Net . The proposed network significantly improves the detection accuracy and robustness for IRSTD by integrating the micro-scale feature encoder MSF Encoder and frequency domain feature enhancement FDFE module. Specifically, the MSF Encoder combines parallel feature extraction and feature enhancement modules to effectively capture multi-scale feature information, thus mitigating information loss. The FDFE module introduces frequency domain features via the Haar wavelet transform, enhancing the semantic differences between targets and backgrounds, thereby improving the distinguishability of small targets. Experimental results on three public datasets, NUAA-SIRST, NUDT-SIRST, and IRSTD-1K, demonstrate that the proposed FDFE-Net outperforms several state-of-the-art IRSTD methods across multiple evaluation metrics.