11,951 research outputs found
Exact Reconstruction using Beurling Minimal Extrapolation
We show that measures with finite support on the real line are the unique
solution to an algorithm, named generalized minimal extrapolation, involving
only a finite number of generalized moments (which encompass the standard
moments, the Laplace transform, the Stieltjes transformation, etc). Generalized
minimal extrapolation shares related geometric properties with basis pursuit of
Chen, Donoho and Saunders [CDS98]. Indeed we also extend some standard results
of compressed sensing (the dual polynomial, the nullspace property) to the
signed measure framework. We express exact reconstruction in terms of a simple
interpolation problem. We prove that every nonnegative measure, supported by a
set containing s points,can be exactly recovered from only 2s + 1 generalized
moments. This result leads to a new construction of deterministic sensing
matrices for compressed sensing.Comment: 27 pages, 3 figures version 2 : minor changes and new titl
Compressed Sensing of Approximately-Sparse Signals: Phase Transitions and Optimal Reconstruction
Compressed sensing is designed to measure sparse signals directly in a
compressed form. However, most signals of interest are only "approximately
sparse", i.e. even though the signal contains only a small fraction of relevant
(large) components the other components are not strictly equal to zero, but are
only close to zero. In this paper we model the approximately sparse signal with
a Gaussian distribution of small components, and we study its compressed
sensing with dense random matrices. We use replica calculations to determine
the mean-squared error of the Bayes-optimal reconstruction for such signals, as
a function of the variance of the small components, the density of large
components and the measurement rate. We then use the G-AMP algorithm and we
quantify the region of parameters for which this algorithm achieves optimality
(for large systems). Finally, we show that in the region where the GAMP for the
homogeneous measurement matrices is not optimal, a special "seeding" design of
a spatially-coupled measurement matrix allows to restore optimality.Comment: 8 pages, 10 figure
Generalized Inpainting Method for Hyperspectral Image Acquisition
A recently designed hyperspectral imaging device enables multiplexed
acquisition of an entire data volume in a single snapshot thanks to
monolithically-integrated spectral filters. Such an agile imaging technique
comes at the cost of a reduced spatial resolution and the need for a
demosaicing procedure on its interleaved data. In this work, we address both
issues and propose an approach inspired by recent developments in compressed
sensing and analysis sparse models. We formulate our superresolution and
demosaicing task as a 3-D generalized inpainting problem. Interestingly, the
target spatial resolution can be adjusted for mitigating the compression level
of our sensing. The reconstruction procedure uses a fast greedy method called
Pseudo-inverse IHT. We also show on simulations that a random arrangement of
the spectral filters on the sensor is preferable to regular mosaic layout as it
improves the quality of the reconstruction. The efficiency of our technique is
demonstrated through numerical experiments on both synthetic and real data as
acquired by the snapshot imager.Comment: Keywords: Hyperspectral, inpainting, iterative hard thresholding,
sparse models, CMOS, Fabry-P\'ero
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