LFDT-Fusion: A Latent Feature-guided Diffusion Transformer Model for general image fusion
Information Fusion,
Год журнала:
2024,
Номер
113, С. 102639 - 102639
Опубликована: Авг. 16, 2024
Язык: Английский
Detail-aware near infrared and visible fusion with multi-order hyper-Laplacian priors
Information Fusion,
Год журнала:
2023,
Номер
99, С. 101851 - 101851
Опубликована: Май 23, 2023
Язык: Английский
A low-light image enhancement framework based on hybrid multiscale decomposition and adaptive brightness adjustment model
Optics & Laser Technology,
Год журнала:
2025,
Номер
185, С. 112621 - 112621
Опубликована: Фев. 18, 2025
Язык: Английский
Detail-preserving noise suppression post-processing for low-light image enhancement
Displays,
Год журнала:
2024,
Номер
83, С. 102738 - 102738
Опубликована: Май 9, 2024
Язык: Английский
Semantic-agnostic progressive subtractive network for image manipulation detection and localization
Neurocomputing,
Год журнала:
2023,
Номер
543, С. 126263 - 126263
Опубликована: Апрель 27, 2023
Язык: Английский
Multimodal image fusion based on diffusion model
Опубликована: Июнь 28, 2024
A saturation-light enhancement method for low-light image via atmospheric scattering model
Optics and Lasers in Engineering,
Год журнала:
2024,
Номер
183, С. 108488 - 108488
Опубликована: Авг. 13, 2024
Язык: Английский
RMANet: Refined-mixed attention network for progressive low-light image enhancement
Signal Processing,
Год журнала:
2024,
Номер
227, С. 109689 - 109689
Опубликована: Сен. 6, 2024
Язык: Английский
A customized mask signal connected components labelingalgorithm
Signal Processing,
Год журнала:
2024,
Номер
230, С. 109845 - 109845
Опубликована: Дек. 9, 2024
Язык: Английский
DIF-LIM: A Dual Information Flow Network for Low-Light Image Enhancement
2021 IEEE International Conference on Robotics and Biomimetics (ROBIO),
Год журнала:
2023,
Номер
unknown, С. 1 - 6
Опубликована: Дек. 4, 2023
Low-light
image
enhancement
(LIE)
aims
to
enhance
contrast
and
recover
fine
details
for
images
captured
in
low-light
conditions.
However,
the
limitations
of
using
a
single
manually
defined
priors
lead
inadequate
information
limited
adaptability,
resulting
failure
reveal
effectively.
To
tackle
this
problem,
we
propose
an
unsupervised
dual
flow
LIE
model
that
learns
adaptive
from
pairs.
The
is
based
on
assumption
can
be
learned
pairs
images.
Moreover,
simple
self+supervised
designed
perform
feature
processing
original
input
further
avoid
suboptimal
model.
As
result,
our
exhibits
superior
performance
task
compared
other
algorithms.
improved
network
adaptability
Язык: Английский