795 citations · 1.4k across the 64 of their papers we have counts for
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Iterative and greedy algorithms for the sparsity in levels model in compressed sensing
Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
Motivated by the question of optimal functional approximation via compressed sensing, we propose generalizations of the Iterative Hard Thresholding and the Compressive Sampling Mat…
Improved recovery guarantees and sampling strategies for TV minimization in compressive imaging
Ben Adcock, Nick Dexter, Qinghong Xu
In this paper, we consider the use of Total Variation (TV) minimization for compressive imaging; that is, image reconstruction from subsampled measurements. Focusing on two importa…
The benefits of acting locally: Reconstruction algorithms for sparse in levels signals with stable and robust recovery guarantees
Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
The sparsity in levels model recently inspired a new generation of effective acquisition and reconstruction modalities for compressive imaging. Moreover, it naturally arises in var…
Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing
Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
A signature result in compressed sensing is that Gaussian random sampling achieves stable and robust recovery of sparse vectors under optimal conditions on the number of measuremen…
Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling
Ben Adcock, Vegard Antun, Anders C. Hansen
Infinite-dimensional compressed sensing deals with the recovery of analog signals (functions) from linear measurements, often in the form of integral transforms such as the Fourier…
On oracle-type local recovery guarantees in compressed sensing
Ben Adcock, Claire Boyer, Simone Brugiapaglia
We present improved sampling complexity bounds for stable and robust sparse recovery in compressed sensing. Our unified analysis based on l1 minimization encompasses the case where…
Robustness to unknown error in sparse regularization
Simone Brugiapaglia, Ben Adcock
Quadratically-constrained basis pursuit has become a popular device in sparse regularization; in particular, in the context of compressed sensing. However, the majority of theoreti…
Analyzing the structure of multidimensional compressed sensing problems through coherence
Alex Jones, Ben Adcock, Anders Hansen
Recently it has been established that asymptotic incoherence can be used to facilitate subsampling, in order to optimize reconstruction quality, in a variety of continuous compress…
Uniform Recovery from Subgaussian Multi-Sensor Measurements
Il Yong Chun, Ben Adcock
Parallel acquisition systems are employed successfully in a variety of different sensing applications when a single sensor cannot provide enough measurements for a high-quality rec…
Sparsity and Parallel Acquisition: Optimal Uniform and Nonuniform Recovery Guarantees
Il Yong Chun, Chen Li, Ben Adcock
The problem of multiple sensors simultaneously acquiring measurements of a single object can be found in many applications. In this paper, we present the optimal recovery guarantee…
Optimal Sparse Recovery for Multi-Sensor Measurements
Il Yong Chun, Ben Adcock
Many practical sensing applications involve multiple sensors simultaneously acquiring measurements of a single object. Conversely, most existing sparse recovery guarantees in compr…
Compressed Sensing and Parallel Acquisition
Il Yong Chun, Ben Adcock
Parallel acquisition systems arise in various applications in order to moderate problems caused by insufficient measurements in single-sensor systems. These systems allow simultane…
Compressed sensing with local structure: uniform recovery guarantees for the sparsity in levels class
Chen Li, Ben Adcock
In compressed sensing, it is often desirable to consider signals possessing additional structure beyond sparsity. One such structured signal model - which forms the focus of this p…
Breaking the coherence barrier: A new theory for compressed sensing
Ben Adcock, Anders C. Hansen, Clarice Poon +1
This paper provides an extension of compressed sensing which bridges a substantial gap between existing theory and its current use in real-world applications. It introduces a mathe…