Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Joint Convolutional and Spatial Quad-Directional LSTM Network for
  Phase Unwrapping   Phase unwrapping is a classical ill-posed problem which aims to recover the
true phase from wrapped phase. In this paper, we introduce a novel
Convolutional Neural Network (CNN) that incorporates a Spatial Quad-Directional
Long Short Term Memory (SQD-LSTM) for phase unwrapping, by formulating it as a
regression problem. Incorporating SQD-LSTM can circumvent the typical CNNs'
inherent difficulty of learning global spatial dependencies which are vital
when recovering the true phase. Furthermore, we employ a problem specific
composite loss function to train this network. The proposed network is found to
be performing better than the existing methods under severe noise conditions
(Normalized Root Mean Square Error of 1.3 % at SNR = 0 dB) while spending a
significantly less computational time (0.054 s). The network also does not
require a large scale dataset during training, thus making it ideal for
applications with limited data that require fast and accurate phase unwrapping.

35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th