DSCSNet: A Dynamic Sparse Compression Sensing Network for Closely-Spaced Infrared Small Target Unmixing

Abstract

Due to the limitations of optical lens focal length and detector resolution, distant clustered infrared small targets often appear as mixed spots. The Close Small Object Unmixing (CSOU) task aims to recover the number, sub-pixel positions, and radiant intensities of individual targets from these spots, which is a highly ill-posed inverse problem. Existing methods struggle to balance the rigorous sparsity guarantees of model-driven approaches and the dynamic scene adaptability of data-driven methods. To address this dilemma, this paper proposes a Dynamic Sparse Compressed Sensing Network (DSCSNet), a deep-unfolded network that couples the Alternating Direction Method of Multipliers (ADMM) with learnable parameters. Specifically, we embed a strict $\ell_1$-norm sparsity constraint into the auxiliary variable update step of ADMM to replace the traditional $\ell_2$-norm smoothness-promoting terms, which effectively preserves the discrete energy peaks of small targets. We also integrate a self-attention-based dynamic thresholding mechanism into the reconstruction stage, which adaptively adjusts the sparsification intensity using the sparsity-enhanced information from the iterative process. These modules are jointly optimized end-to-end across the three iterative steps of ADMM. Retaining the physical logic of compressed sensing, DSCSNet achieves robust sparsity induction and scene adaptability, thus enhancing the unmixing accuracy and generalization in complex infrared scenarios. Extensive experiments on the synthetic infrared dataset CSIST-100K demonstrate that DSCSNet outperforms state-of-the-art methods in key metrics such as CSO-mAP and sub-pixel localization error.

Figures (8)

• Figure 1: The process of imaging mixing and unmixing of Closed-spaced targets. The green box represents the process of mixing distant neighboring targets in the infrared system, menawhile, the blue box represents the process of unmixing. The input for unmixing is the mixed image, and the output is the number of small targets in the mixed light spot, as well as the sub-pixel position and radiation intensity of each small target.
• Figure 2: The first column shows that IRSTD methods assume a one-to-one correspondence between detected targets and real-world objects; when only one spot appears in the image, the detection result corresponds to a single target. The super-resolution approach in the second column can split the mixed spot into several sub-targets, yet its sub-pixel localization accuracy is insufficient, leading to missed detections and false alarms. In contrast, the DCSCNet unmixing method in the third column delivers more refined detection, enabling sub-pixel localization within the spot and unmixing of potential sub-targets.
• Figure 3: Schematic of DCSCNet unmixing, organized into three main stages—initialize_layer, update_layer, and reconstruction_layer. Within each stage, three embedded sub-layers perform the ADMM variable updates for input image X, auxiliary variable Z, and multiplier u.
• Figure 4: Architecture of the proposed Dynamic Convolutional Sparse Coding Network (DCSCNet).
• Figure 5: Each pixel is subdivided into an 3 × 3 sub-pixel grid that represents the set of potential target locations.