LSDSSMs: Infrared Small Target Detection Network Based on Low-Rank Sparse Decomposition State-Space Models

Abstract

In recent years, infrared small target detection (ISTD) networks based on deep learning (DL) have achieved notable advances. However, these methods still face significant challenges when applied to the real world. Most of them lack the fundamental principles of small target detection in infrared imagery, which results in difficulties in distinguishing targets from complex backgrounds and poor interpretability. To address these challenges, an interpretable network architecture for ISTD, termed low-rank sparse decomposition state-space models (LSDSSMs), is proposed. LSDSSMs use the principles of low-rank and sparse decomposition, incorporating dedicated modules for the low-rank space separation module and the sparse target extraction (STE) module. These modules facilitate the extraction of sparse representations for both low-rank backgrounds and small targets. In addition, a joint reconstruction (JR) module is used to integrate these components, generating reconstructed images. Considering the unique imaging characteristics of infrared images and the sparse nature of small targets, a channel selection module (CSM) is proposed to enhance the extraction of sparse targets. To enhance the adaptability, stability, and resistance of LSDSSMs in complex environments, robust state-space models (SSMs) are integrated that combine local and global information representations. Furthermore, a multilevel loss function is introduced to enforce comprehensive constraints on low-rank backgrounds, sparse targets, and reconstructed images. This design improves not only the robustness of the LSDSSMs but also its performance across different scenarios. Extensive experimental results demonstrate that LSDSSMs surpass existing baseline methods in both qualitative and quantitative assessments, validating their effectiveness and reliability.