323 citations
- Claremont Graduate UniversityUS16 papers
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8 papers · 1 filter
Constrained adaptive sensing
Mark A. Davenport, Andrew K. Massimino, Deanna Needell +1
Suppose that we wish to estimate a vector from a small number of noisy linear measurements of the form , where…
Compressive Sensing with Redundant Dictionaries and Structured Measurements
Felix Krahmer, Deanna Needell, Rachel Ward
Consider the problem of recovering an unknown signal from undersampled measurements, given the knowledge that the signal has a sparse representation in a specified dictionary .…
Exponential decay of reconstruction error from binary measurements of sparse signals
Richard Baraniuk, Simon Foucart, Deanna Needell +2
Binary measurements arise naturally in a variety of statistical and engineering applications. They may be inherent to the problem---e.g., in determining the relationship between ge…
Two-Part Reconstruction with Noisy-Sudocodes
Yanting Ma, Dror Baron, Deanna Needell
We develop a two-part reconstruction framework for signal recovery in compressed sensing (CS), where a fast algorithm is applied to provide partial recovery in Part 1, and a CS alg…
Two-Part Reconstruction in Compressed Sensing
Yanting Ma, Dror Baron, Deanna Needell
Two-part reconstruction is a framework for signal recovery in compressed sensing (CS), in which the advantages of two different algorithms are combined. Our framework allows to acc…
Super-resolution via superset selection and pruning
Laurent Demanet, Deanna Needell, Nam Nguyen
We present a pursuit-like algorithm that we call the "superset method" for recovery of sparse vectors from consecutive Fourier measurements in the super-resolution regime. The algo…