Lightweight Local–Global Dual-Path Feature Fusion Network for Infrared Small Target Image Super-Resolution and Enhancement

The infrared imaging is widely used in remote sensing and military target recognition due to its strong resistance to interference in complex environments. However, imaging mechanisms and hardware limitations cause infrared images to have low resolution, sparse textures, and significant background noise, which severely restrict the detection of small targets, such as low-altitude drones and weak thermal emitters. To overcome these limitations, we propose a lightweight local–global dual-path feature fusion network (LDFF-Net) that enhances the resolution and quality of infrared images, providing high-quality inputs for subsequent detection tasks. The network includes a small target feature recognition module (STFRM) composed of three key components. The enhanced high frequency perception module (EHFPM) strengthens high-frequency details of small targets while suppressing noise, enabling the robust local feature extraction. The state-space model (SSM) captures long-range dependencies with linear complexity and models semantic relationships between targets and background to compensate for the limited receptive field of local features. The adaptive feature fusion unit (AFFU) combines local and global features adaptively to improve the saliency of small targets. During training, we introduce a realistic degradation process based on visible-light images to generate training samples that include complex degradation patterns and noise, which enhances the model’s robustness and generalization. Evaluation on the ARCHIVE and SIRST datasets demonstrates that LDFF-Net outperforms existing state-of-the-art methods across eight widely used full-reference and no-reference metrics, including peak signal-to-noise ratio (PSNR), learned perceptual image patch similarity (LPIPS), fréchet inception distance (FID), and natural image quality evaluator (NIQE). This result confirms the model’s effectiveness in enhancing both the super-resolution (SR) and detection performance of infrared small target images.