Transparent
objects
are
extensively
utilized
across
various
aspects,
yet
their
non-destructive
optical
measurement
remains
challenging.
In-line
lensless
digital
holography
has
emerged
as
an
efficient
and
precise
technique
for
detecting
transparent
objects,
with
the
advantages
of
simpler
device
requirements
more
effective
utilization
detector
limited
spacebandwidth
product.
However,
presence
twin-image
significantly
degrades
quality
reconstructed
images.
Conventional
approaches
to
mitigating
require
intricate
hardware
configurations
or
time-consuming
algorithms.
In
this
paper,
we
proposed
a
new
network
called
Attention
mechanism
in
Convolutional
neural
Network
(ACNet),
which
provides
fast
deep
learning
solution
twin
image
suppression.
The
approach
numerically
generated
datasets
training
convolutional
(CNN)
was
employed
attention
perform
removal.
Simulation
results
demonstrate
that
method
effectively
eliminates
interference
phase
recovery,
thereby
enhances
reconstruction
in-line
holography.
present
work
great
potentials
wider
applications
Biomedical Optics Express,
Год журнала:
2024,
Номер
16(1), С. 222 - 222
Опубликована: Ноя. 13, 2024
A
fair
comparison
of
multiple
live
cell
cultures
requires
examining
them
under
identical
environmental
conditions,
which
can
only
be
done
accurately
if
all
cells
are
prepared
simultaneously
and
studied
at
the
same
time
place.
This
contribution
introduces
a
multiplexed
lensless
digital
holographic
microscopy
system
(MLS),
enabling
synchronous,
label-free,
quantitative
observation
with
single-cell
precision.
The
innovation
this
setup
lies
in
its
ability
to
robustly
compare
behaviour,
i.e.,
migratory
pathways,
cultured
or
contained
different
ways
(with
varied
stimuli
applied),
making
it
valuable
tool
for
dynamic
biomedical
diagnostics
on
cellular
level.
system's
design
allows
potential
expansion
accommodate
as
many
samples
needed,
thus
broadening
application
scope
future
global
multi-culture
behaviours
via
their
localized
spatiotemporal
optical
signatures.
We
believe
that
our
method
has
empower
reliable
comparisons
through
simultaneous
imaging,
enhancing
label-free
investigations
into
effects
biochemical
physical
over
large
areas,
unlocking
novel
mechanistic
understandings
high-throughput
time-lapse
observations.
This
study
introduces
a
methodology
for
quantitative
assessment
of
phase
measurement
sensitivity
in
lensless
digital
holographic
microscopy
(LDHM)
setups,
incorporating
an
immersion
medium
between
the
object
and
detector.
Utilizing
two
setup
configurations,
we
systematically
investigated
influence
conditions
on
accuracy,
numerical
reconstruction,
twin-image
artefacts.
Employing
Angular
Spectrum
iterative
Gerchberg-Saxton
methods,
reconstructed
maps
varying
thicknesses.
Results
demonstrate
that
has
minimal
but
significantly
reduces
artifacts
when
direct
contact
with
object,
providing
valuable
insights
developing
LDHM
biological
applications.
Optics Express,
Год журнала:
2024,
Номер
32(10), С. 17255 - 17255
Опубликована: Фев. 21, 2024
This
joint
feature
issue
of
Optics
Express
and
Applied
showcases
technical
innovations
by
participants
the
2023
topical
meeting
on
Computational
Optical
Sensing
Imaging
computational
imaging
community.
The
articles
included
in
highlight
advances
science
that
emphasize
synergistic
activities
optics,
signal
processing
machine
learning.
features
26
contributed
cover
multiple
themes
including
non
line-of-sight
imaging,
through
scattering
media,
compressed
sensing,
lensless
ptychography,
microscopy,
spectroscopy
optical
metrology.
Applied Optics,
Год журнала:
2024,
Номер
63(8), С. COSI1 - COSI1
Опубликована: Фев. 21, 2024
This
joint
feature
issue
of
Optics
Express
and
Applied
showcases
technical
innovations
by
participants
the
2023
topical
meeting
on
Computational
Optical
Sensing
Imaging
computational
imaging
community.
The
articles
included
in
highlight
advances
science
that
emphasize
synergistic
activities
optics,
signal
processing
machine
learning.
features
26
contributed
cover
multiple
themes
including
non
line-of-sight
imaging,
through
scattering
media,
compressed
sensing,
lensless
ptychography,
microscopy,
spectroscopy
optical
metrology.
This
work
presents
an
in-line
holographic
reconstruction
method
for
low
signal-to-noise
ratio
data.
Algorithm
is
positively
validated
in
terms
of
shot
noise
suppression,
twin
image
minimization
and
high
lateral
resolution.
Transparent
objects
are
extensively
utilized
across
various
aspects,
yet
their
non-destructive
optical
measurement
remains
challenging.
In-line
lensless
digital
holography
has
emerged
as
an
efficient
and
precise
technique
for
detecting
transparent
objects,
with
the
advantages
of
simpler
device
requirements
more
effective
utilization
detector
limited
spacebandwidth
product.
However,
presence
twin-image
significantly
degrades
quality
reconstructed
images.
Conventional
approaches
to
mitigating
require
intricate
hardware
configurations
or
time-consuming
algorithms.
In
this
paper,
we
proposed
a
new
network
called
Attention
mechanism
in
Convolutional
neural
Network
(ACNet),
which
provides
fast
deep
learning
solution
twin
image
suppression.
The
approach
numerically
generated
datasets
training
convolutional
(CNN)
was
employed
attention
perform
removal.
Simulation
results
demonstrate
that
method
effectively
eliminates
interference
phase
recovery,
thereby
enhances
reconstruction
in-line
holography.
present
work
great
potentials
wider
applications
