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arXiv · 2401.11805

Simultaneous Blind Demixing and Super-resolution via Vectorized Hankel Lift

Haifeng Wang·Jinchi Chen·Hulei Fan·Yuxiang Zhao·Li Yu
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Abstract

In this work, we investigate the problem of simultaneous blind demixing and super-resolution. Leveraging the subspace assumption regarding unknown point spread functions, this problem can be reformulated as a low-rank matrix demixing problem. We propose a convex recovery approach that utilizes the low-rank structure of each vectorized Hankel matrix associated with the target matrix. Our analysis reveals that for achieving exact recovery, the number of samples needs to satisfy the condition $n\gtrsim Ksr \log (sn)$. Empirical evaluations demonstrate the recovery capabilities and the computational efficiency of the convex method.

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Haifeng Wang, Jinchi Chen, Hulei Fan, Yuxiang Zhao, Li Yu. 2024-01-22. Simultaneous Blind Demixing and Super-resolution via Vectorized Hankel Lift. https://arxiv.org/abs/2401.11805

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