ATCM-Net: A deep learning method for phase unwrapping based on perception optimization and learning enhancement
Min Xu,
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Jia Cong,
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Yuxin Shen
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et al.
Optics & Laser Technology,
Journal Year:
2025,
Volume and Issue:
190, P. 113185 - 113185
Published: May 19, 2025
Language: Английский
A divided difference filter-based phase unwrapping method
Xianming Xie,
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Rong Li,
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Luo Guoping
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et al.
Optics and Lasers in Engineering,
Journal Year:
2024,
Volume and Issue:
176, P. 108114 - 108114
Published: Feb. 14, 2024
Language: Английский
Phase unwrapping via fully exploiting global and local spatial dependencies
Yuhui Quan,
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Xin Yao,
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Zhifeng Chen
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et al.
Optics & Laser Technology,
Journal Year:
2024,
Volume and Issue:
181, P. 111872 - 111872
Published: Oct. 2, 2024
Language: Английский
Phase unwrapping via deep learning for surface shape measurement by using wavelength tuning interferometry
Bohang Zhong,
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Huaian Yi,
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Seokyoung Ahn
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et al.
Published: Dec. 30, 2023
Wavelength
tuning
interferometry
is
widely
used
in
optical
metrology
order
to
obtain
the
phase
information
of
sample.
The
obtained
wrapped
usually
unwraps
range
[-π,
π].
Therefore,
unwrapping
operation
required
right
phase.
But
some
factors,
such
as
shift
miscalibration,
coupling
error,
and
noise,
always
lower
precision
conventional
shifting
algorithm.
To
address
kind
problems,
we
proposed
a
deep
learning
method
using
convolutional
neural
network
perform
process
by
turning
task
into
multiclass
classification
work
2N
-
1
for
generating
training
dataset.
results
indicated
that
not
only
can
compensate
miscalibration
but
also
has
strong
robust
denoise
ability,
which
means
outperformed
other
measurement
algorithms.
Language: Английский