Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Classification and reconstruction of spatially overlapping phase images
  using diffractive optical networks   Diffractive optical networks unify wave optics and deep learning to
all-optically compute a given machine learning or computational imaging task as
the light propagates from the input to the output plane. Here, we report the
design of diffractive optical networks for the classification and
reconstruction of spatially overlapping, phase-encoded objects. When two
different phase-only objects spatially overlap, the individual object functions
are perturbed since their phase patterns are summed up. The retrieval of the
underlying phase images from solely the overlapping phase distribution presents
a challenging problem, the solution of which is generally not unique. We show
that through a task-specific training process, passive diffractive networks
composed of successive transmissive layers can all-optically and simultaneously
classify two different randomly-selected, spatially overlapping phase images at
the input. After trained with ~550 million unique combinations of phase-encoded
handwritten digits from the MNIST dataset, our blind testing results reveal
that the diffractive network achieves an accuracy of >85.8% for all-optical
classification of two overlapping phase images of new handwritten digits. In
addition to all-optical classification of overlapping phase objects, we also
demonstrate the reconstruction of these phase images based on a shallow
electronic neural network that uses the highly compressed output of the
diffractive network as its input (with e.g., ~20-65 times less number of
pixels) to rapidly reconstruct both of the phase images, despite their spatial
overlap and related phase ambiguity. The presented phase image classification
and reconstruction framework might find applications in e.g., computational
imaging, microscopy and quantitative phase imaging fields.

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The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th