Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extraction of digital wavefront sets using applied harmonic analysis and
  deep neural networks   Microlocal analysis provides deep insight into singularity structures and is
often crucial for solving inverse problems, predominately, in imaging sciences.
Of particular importance is the analysis of wavefront sets and the correct
extraction of those. In this paper, we introduce the first algorithmic approach
to extract the wavefront set of images, which combines data-based and
model-based methods. Based on a celebrated property of the shearlet transform
to unravel information on the wavefront set, we extract the wavefront set of an
image by first applying a discrete shearlet transform and then feeding local
patches of this transform to a deep convolutional neural network trained on
labeled data. The resulting algorithm outperforms all competing algorithms in
edge-orientation and ramp-orientation detection.

6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn