Journal of Function Spaces,
Год журнала:
2022,
Номер
2022, С. 1 - 11
Опубликована: Дек. 31, 2022
This
paper
considers
a
multichannel
deconvolution
model
with
Gaussian
white
noises.
The
goal
is
to
estimate
the
d
-th
derivatives
of
an
unknown
function
in
model.
For
super-smooth
case,
we
construct
adaptive
linear
wavelet
estimator
by
projection
method.
regular-smooth
provide
nonlinear
hard-thresholded
In
order
measure
global
performances
our
estimators,
show
upper
bounds
on
convergence
rates
using
Lp
-risk
(
1≤p<∞
).
Light Science & Applications,
Год журнала:
2024,
Номер
13(1)
Опубликована: Янв. 1, 2024
Phase
recovery
(PR)
refers
to
calculating
the
phase
of
light
field
from
its
intensity
measurements.
As
exemplified
quantitative
imaging
and
coherent
diffraction
adaptive
optics,
PR
is
essential
for
reconstructing
refractive
index
distribution
or
topography
an
object
correcting
aberration
system.
In
recent
years,
deep
learning
(DL),
often
implemented
through
neural
networks,
has
provided
unprecedented
support
computational
imaging,
leading
more
efficient
solutions
various
problems.
this
review,
we
first
briefly
introduce
conventional
methods
PR.
Then,
review
how
DL
provides
following
three
stages,
namely,
pre-processing,
in-processing,
post-processing.
We
also
used
in
image
processing.
Finally,
summarize
work
provide
outlook
on
better
use
improve
reliability
efficiency
Furthermore,
present
a
live-updating
resource
(
https://github.com/kqwang/phase-recovery
)
readers
learn
about
Applied Optics,
Год журнала:
2023,
Номер
62(20), С. 5433 - 5433
Опубликована: Июнь 20, 2023
Reliable
detection
of
defects
from
optical
fringe
patterns
is
a
crucial
problem
in
non-destructive
interferometric
metrology.
In
this
work,
we
propose
deep-learning-based
method
for
pattern
defect
identification.
By
attributing
the
information
to
pattern's
phase
gradient,
compute
spatial
derivatives
using
deep
learning
model
and
apply
gradient
map
localize
defect.
The
robustness
proposed
illustrated
on
multiple
numerically
synthesized
at
various
noise
levels.
Further,
practical
utility
substantiated
experimental
identification
diffraction
microscopy.
Optics Continuum,
Год журнала:
2024,
Номер
3(9), С. 1765 - 1765
Опубликована: Сен. 4, 2024
In
digital
holographic
interferometry,
the
measurement
of
derivatives
interference
phase
plays
a
crucial
role
in
deformation
testing
since
displacement
corresponding
to
deformed
object
are
directly
related
derivatives.
this
work,
we
propose
recurrent
neural
network-assisted
state
space
method
for
reliable
estimation
The
proposed
offers
high
robustness
against
severe
noise
and
corrupted
fringe
data
regions,
its
performance
is
validated
via
numerical
simulations.
We
also
corroborate
practical
applicability
by
analyzing
experimental
test
objects
interferometry.
Journal of the Optical Society of America A,
Год журнала:
2023,
Номер
40(3), С. 611 - 611
Опубликована: Янв. 31, 2023
In
quantitative
phase
microscopy,
measurement
of
the
gradient
is
an
important
problem
for
biological
cell
morphological
studies.
this
paper,
we
propose
a
method
based
on
deep
learning
approach
that
capable
direct
estimation
without
requirement
unwrapping
and
numerical
differentiation
operations.
We
show
robustness
proposed
using
simulations
under
severe
noise
conditions.
Further,
demonstrate
method's
utility
imaging
different
cells
diffraction
microscopy
setup.
