DSCSNet: A Dynamic Sparse Compression Sensing Network for Closely-Spaced Infrared Small Target Unmixing
Zhiyang Tang, Yiming Zhu, Ruimin Huang, Meng Yang, Yong Ma, Jun Huang, Fan Fan

TL;DR
This paper introduces DSCSNet, a deep-unfolded network combining ADMM and learnable parameters to improve the unmixing of closely-spaced infrared small targets, balancing sparsity and scene adaptability.
Contribution
The paper proposes a novel deep-unfolded network with a strict $$-norm sparsity constraint and self-attention-based dynamic thresholding for infrared small target unmixing.
Findings
Outperforms state-of-the-art methods in CSO-mAP
Achieves lower sub-pixel localization error
Demonstrates robustness in complex infrared scenarios
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 -norm sparsity constraint into the auxiliary variable update step of ADMM to replace the traditional -norm smoothness-promoting…
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Taxonomy
TopicsInfrared Target Detection Methodologies · Sparse and Compressive Sensing Techniques · Advanced optical system design
