324 citations · 1.4k across the 135 of their papers we have counts for
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Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomography
T. A. Bubba, G. Kutyniok, M. Lassas +4
The high complexity of various inverse problems poses a significant challenge to model-based reconstruction schemes, which in such situations often reach their limits. At the same…
Learning sparse representations on the sphere
Florent Sureau, Felix Voigtlaender, Malte Wust +2
Many representation systems on the sphere have been proposed in the past, such as spherical harmonics, wavelets, or curvelets. Each of these data representations is designed to ext…
The Mismatch Principle: The Generalized Lasso Under Large Model Uncertainties
Martin Genzel, Gitta Kutyniok
We study the estimation capacity of the generalized Lasso, i.e., least squares minimization combined with a (convex) structural constraint. While Lasso-type estimators were origina…
Hierarchical Sparse Channel Estimation for Massive MIMO
Gerhard Wunder, Ingo Roth, Axel Flinth +4
The problem of wideband massive MIMO channel estimation is considered. Targeting for low complexity algorithms as well as small training overhead, a compressive sensing (CS) approa…
Compressed Sensing for Analog Signals
Bernard G. Bodmann, Axel Flinth, Gitta Kutyniok
In this paper we develop a general theory of compressed sensing for analog signals, in close similarity to prior results for vectors in finite dimensional spaces that are sparse in…