Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Performance Bounds For Co-/Sparse Box Constrained Signal Recovery   The recovery of structured signals from a few linear measurements is a
central point in both compressed sensing (CS) and discrete tomography. In CS
the signal structure is described by means of a low complexity model e.g.
co-/sparsity. The CS theory shows that any signal/image can be undersampled at
a rate dependent on its intrinsic complexity. Moreover, in such undersampling
regimes, the signal can be recovered by sparsity promoting convex
regularization like $\ell_1$- or total variation (TV-) minimization. Precise
relations between many low complexity measures and the sufficient number of
random measurements are known for many sparsity promoting norms. However, a
precise estimate of the undersampling rate for the TV seminorm is still
lacking. We address this issue by: a) providing dual certificates testing
uniqueness of a given cosparse signal with bounded signal values, b)
approximating the undersampling rates via the statistical dimension of the TV
descent cone and c) showing empirically that the provided rates also hold for
tomographic measurements.

27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an