Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Analysis of simultaneous inpainting and geometric separation based on
  sparse decomposition   Natural images are often the superposition of various parts of different
geometric characteristics. For instance, an image might be a mixture of cartoon
and texture structures. In addition, images are often given with missing data.
In this paper, we develop a method for simultaneously decomposing an image to
its two underlying parts and inpainting the missing data. Our separation
inpainting method is based on and $l_1$ minimization approach, using two
dictionaries, each sparsifying one of the image parts but not the other. We
introduce a comprehensive convergence analysis of our method, in a general
setting, utilizing the concepts of joint concentration, clustered sparsity, and
cluster coherence. As the main application of our theory, we consider the
problem of separating and inpainting an image to a cartoon and texture parts.

13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas