Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Atmospheric Turbulence Removal with Complex-Valued Convolutional Neural
  Network   Atmospheric turbulence distorts visual imagery and is always problematic for
information interpretation by both human and machine. Most well-developed
approaches to remove atmospheric turbulence distortion are model-based.
However, these methods require high computation and large memory preventing
their feasibility of real-time operation. Deep learning-based approaches have
hence gained more attention but currently work efficiently only on static
scenes. This paper presents a novel learning-based framework offering short
temporal spanning to support dynamic scenes. We exploit complex-valued
convolutions as phase information, altered by atmospheric turbulence, is
captured better than using ordinary real-valued convolutions. Two concatenated
modules are proposed. The first module aims to remove geometric distortions
and, if enough memory, the second module is applied to refine micro details of
the videos. Experimental results show that our proposed framework efficiently
mitigate the atmospheric turbulence distortion and significantly outperforms
the existing methods.

13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics