3-D SAR Imaging via Perceptual Learning Framework With Adaptive Sparse Prior
Mathematically, 3-D synthetic aperture radar (SAR) imaging is a typical inverse problem,
which, by nature, can be solved by applying the theory of sparse signal recovery. However …
which, by nature, can be solved by applying the theory of sparse signal recovery. However …
3-D SAR data-driven imaging via learned low-rank and sparse priors
In the research topic of three-dimensional (3-D) synthetic aperture radar (SAR) imaging, the
sparsity-enforcing techniques offer promise in shortening the sensing time and improving …
sparsity-enforcing techniques offer promise in shortening the sensing time and improving …
Learning-Based Sparse Recovery Algorithm for 3D SAR Imaging
Z Zhou, S Wei, H Zhang, R Shen, J Shi… - IGARSS 2022-2022 …, 2022 - ieeexplore.ieee.org
The compressed sensing (CS) method is widely utilized in the field of radar sparse imaging.
However, it always encounters enormous iterations and low generalizability. To solve these …
However, it always encounters enormous iterations and low generalizability. To solve these …
SAR Non-Sparse Scene Reconstruction Network via Image Feature Representation Learning
J Yang, H Zuo, H An, R Jiang, Z Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) is widely used in various fields due to its all-weather and all-
day working characteristics. With the increasing use of SAR on small platforms, SAR is …
day working characteristics. With the increasing use of SAR on small platforms, SAR is …
SEIS-Net: A 3D SAR Enhance Imaging Network based on Swin-Transformer
Y Hu, M Wang, S Wei, J Li… - IEEE Journal of Selected …, 2024 - ieeexplore.ieee.org
Conventional 3-D synthetic aperture radar (SAR) sparse imaging algorithms suffer from
degradation in weakly sparse scenes due to their reliance on inherent sparsity. In addition …
degradation in weakly sparse scenes due to their reliance on inherent sparsity. In addition …
SR-ISTA-Net: Sparse representation-based deep learning approach for SAR imaging
H Zhang, J Ni, S Xiong, Y Luo… - IEEE Geoscience and …, 2022 - ieeexplore.ieee.org
Compressed sensing (CS) reconstruction of nonsparse scenes is one of the difficulties in
synthetic aperture radar (SAR) imaging technology. Although the conventional CS method …
synthetic aperture radar (SAR) imaging technology. Although the conventional CS method …
TPSSI-Net: Fast and enhanced two-path iterative network for 3D SAR sparse imaging
The emerging field of combining compressed sensing (CS) and three-dimensional synthetic
aperture radar (3D SAR) imaging has shown significant potential to reduce sampling rate …
aperture radar (3D SAR) imaging has shown significant potential to reduce sampling rate …
Nonsparse SAR scene imaging network based on sparse representation and approximate observations
H Zhang, J Ni, K Li, Y Luo, Q Zhang - Remote Sensing, 2023 - mdpi.com
Sparse-representation-based synthetic aperture radar (SAR) imaging technology has shown
superior potential in the reconstruction of nonsparse scenes. However, many existing …
superior potential in the reconstruction of nonsparse scenes. However, many existing …
SAR imaging based on deep unfolded network with approximated observation
L Kang, T Sun, Y Luo, J Ni… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Compressed sensing (CS)-based synthetic aperture radar (SAR) imaging methods are
showing superior potential in imaging performance over classical matched filtering-based …
showing superior potential in imaging performance over classical matched filtering-based …
[HTML][HTML] Deep Learning-Based Approximated Observation Sparse SAR Imaging via Complex-Valued Convolutional Neural Network
Z Ji, L Li, H Bi - Remote Sensing, 2024 - mdpi.com
Sparse synthetic aperture radar (SAR) imaging has demonstrated excellent potential in
image quality improvement and data compression. However, conventional observation …
image quality improvement and data compression. However, conventional observation …