IRMamba: Pixel Difference Mamba with Layer Restoration for Infrared Small Target Detection

Abstract

Infrared small target detection (IRSTD) focuses on identifying small targets in infrared images. Despite advancements with deep learning, challenges persist due to the IR long-range imaging mechanism, where targets are small, dim, and easily lost in noise and background clutter. Current deep learning methods struggle to suppress noise and background interference while preserving fine details, leading to missed detections and false alarms. To address these issues, we propose IRMamba, an encoder-decoder architecture featuring Pixel Difference Mamba (PDMamba) and a Layer Restoration Module (LRM). Specifically, PDMamba integrates the intensity and directional information of pixel differences between scanning positions and their central neighborhoods into the state equation of the state space model (SSM). This enhances target detail representation and suppresses background interference by capturing local 2D dependencies from a global perspective. In addition, LRM incorporates the double-depth image prior into the iterative convergence algorithm, and utilizes the inter-layer interrelationships to gradually reverse the separation of the target layer, achieving noise suppression and refined reconstruction of the image mask. Experiments conducted on multiple public datasets, including NUAA-SIRST, NUDT-SIRST, and IRSTD-1K, demonstrate the significant advantages of IRMamba over SOTA methods.