37 citations · 68 across the 11 of their papers we have counts for
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Multichannel Sparse Blind Deconvolution on the Sphere
Yanjun Li, Yoram Bresler
Multichannel blind deconvolution is the problem of recovering an unknown signal and multiple unknown channels from their circular convolution ($i=…
Optimal Sample Complexity for Stable Matrix Recovery
Yanjun Li, Kiryung Lee, Yoram Bresler
Tremendous efforts have been made to study the theoretical and algorithmic aspects of sparse recovery and low-rank matrix recovery. This paper fills a theoretical gap in matrix rec…
Blind Gain and Phase Calibration via Sparse Spectral Methods
Yanjun Li, Kiryung Lee, Yoram Bresler
Blind gain and phase calibration (BGPC) is a bilinear inverse problem involving the determination of unknown gains and phases of the sensing system, and the unknown signal, jointly…
Identifiability in Blind Deconvolution with Subspace or Sparsity Constraints
Yanjun Li, Kiryung Lee, Yoram Bresler
Blind deconvolution (BD), the resolution of a signal and a filter given their convolution, arises in many applications. Without further constraints, BD is ill-posed. In practice, s…
Oblique Pursuits for Compressed Sensing
Kiryung Lee, Yoram Bresler, Marius Junge
Compressed sensing is a new data acquisition paradigm enabling universal, simple, and reduced-cost acquisition, by exploiting a sparse signal model. Most notably, recovery of the s…
Guaranteed Minimum Rank Approximation from Linear Observations by Nuclear Norm Minimization with an Ellipsoidal Constraint
Kiryung Lee, Yoram Bresler
The rank minimization problem is to find the lowest-rank matrix in a given set. Nuclear norm minimization has been proposed as an convex relaxation of rank minimization. Recht, Faz…