Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fourier Phase Retrieval with Extended Support Estimation via Deep Neural
  Network   We consider the problem of sparse phase retrieval from Fourier transform
magnitudes to recover the $k$-sparse signal vector and its support
$\mathcal{T}$. We exploit extended support estimate $\mathcal{E}$ with size
larger than $k$ satisfying $\mathcal{E} \supseteq \mathcal{T}$ and obtained by
a trained deep neural network (DNN). To make the DNN learnable, it provides
$\mathcal{E}$ as the union of equivalent solutions of $\mathcal{T}$ by
utilizing modulo Fourier invariances. Set $\mathcal{E}$ can be estimated with
short running time via the DNN, and support $\mathcal{T}$ can be determined
from the DNN output rather than from the full index set by applying hard
thresholding to $\mathcal{E}$. Thus, the DNN-based extended support estimation
improves the reconstruction performance of the signal with a low complexity
burden dependent on $k$. Numerical results verify that the proposed scheme has
a superior performance with lower complexity compared to local search-based
greedy sparse phase retrieval and a state-of-the-art variant of the Fienup
method.

17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess