Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Ноя. 8, 2024
The
task
of
image
fusion
for
optical
images
and
SAR
is
to
integrate
valuable
information
from
source
images.
Recently,
owing
powerful
generation,
diffusion
models,
e.g.,
denoising
probabilistic
model
score-based
model,
are
flourished
in
processing,
there
some
effective
attempts
by
scholars'
progressive
explorations.
However,
the
models
suffer
inevitable
speckle
that
seriously
shelters
same
location
image.
Besides,
these
methods
pixel-level
features
without
high-level
tasks,
target
detection
classification,
which
leads
fused
insufficient
their
application
accuracies
low,
tasks.
To
tackle
hurdles,
we
propose
semantic
guided
posterior
sampling
fusion.
Firstly,
employ
SAR-BM3D
as
preprocessing
despeckle.
Then,
established
with
fidelity,
regularization
guidance
term.
first
two
terms
obtained
variational
method
via
inference
first-order
stochastic
optimization.
last
term
served
cross
entropy
loss
between
annotation
classification
result
FLCNet
design.
Finally,
experiments
validate
feasibility
superiority
proposed
on
WHU-OPT-SAR
dataset
DDHRNet
dataset.
arXiv (Cornell University),
Год журнала:
2023,
Номер
unknown
Опубликована: Янв. 1, 2023
Pedestrian
detection
plays
a
critical
role
in
computer
vision
as
it
contributes
to
ensuring
traffic
safety.
Existing
methods
that
rely
solely
on
RGB
images
suffer
from
performance
degradation
under
low-light
conditions
due
the
lack
of
useful
information.
To
address
this
issue,
recent
multispectral
approaches
have
combined
thermal
provide
complementary
information
and
obtained
enhanced
performances.
Nevertheless,
few
focus
negative
effects
false
positives
caused
by
noisy
fused
feature
maps.
Different
them,
we
comprehensively
analyze
impacts
find
enhancing
contrast
can
significantly
reduce
these
positives.
In
paper,
propose
novel
target-aware
fusion
strategy
for
pedestrian
detection,
named
TFDet.
TFDet
achieves
state-of-the-art
two
benchmarks,
KAIST
LLVIP.
easily
extend
multi-class
object
scenarios.
It
outperforms
previous
best
FLIR
M3FD.
Importantly,
has
comparable
inference
efficiency
approaches,
remarkably
good
even
conditions,
which
is
significant
advancement
road
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Ноя. 8, 2024
The
task
of
image
fusion
for
optical
images
and
SAR
is
to
integrate
valuable
information
from
source
images.
Recently,
owing
powerful
generation,
diffusion
models,
e.g.,
denoising
probabilistic
model
score-based
model,
are
flourished
in
processing,
there
some
effective
attempts
by
scholars'
progressive
explorations.
However,
the
models
suffer
inevitable
speckle
that
seriously
shelters
same
location
image.
Besides,
these
methods
pixel-level
features
without
high-level
tasks,
target
detection
classification,
which
leads
fused
insufficient
their
application
accuracies
low,
tasks.
To
tackle
hurdles,
we
propose
semantic
guided
posterior
sampling
fusion.
Firstly,
employ
SAR-BM3D
as
preprocessing
despeckle.
Then,
established
with
fidelity,
regularization
guidance
term.
first
two
terms
obtained
variational
method
via
inference
first-order
stochastic
optimization.
last
term
served
cross
entropy
loss
between
annotation
classification
result
FLCNet
design.
Finally,
experiments
validate
feasibility
superiority
proposed
on
WHU-OPT-SAR
dataset
DDHRNet
dataset.
