Simultaneous Blind Demixing and Super-resolution via Vectorized Hankel Lift
CoRR(2024)
摘要
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≳ Ksr log (sn). Empirical
evaluations demonstrate the recovery capabilities and the computational
efficiency of the convex method.
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