Sparse SAR Imaging Algorithm in Marine Environments Based on Memory-Augmented Deep Unfolding Network
Oceanic targets, including ripples, islands, vessels, and coastlines, display distinct sparse
characteristics, rendering the ocean a significant arena for sparse Synthetic Aperture Radar …
characteristics, rendering the ocean a significant arena for sparse Synthetic Aperture Radar …
PnP-MFAMP-Net: A Novel Plug-and-Play Sparse Reconstruction Network for SAR Imaging
H Zhang, J Ni, S Xiong, Y Luo… - IEEE Geoscience and …, 2024 - ieeexplore.ieee.org
Currently, the sparse imaging problem of synthetic aperture radar (SAR) is primarily
addressed by compressed sensing (CS) theory, which introduces prior information into …
addressed by compressed sensing (CS) theory, which introduces prior information into …
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 …
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 …
[HTML][HTML] 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 …
LRSR-ADMM-Net: A joint low-rank and sparse recovery network for SAR imaging
H An, R Jiang, J Wu, KC Teh, Z Sun… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) imaging with sub-Nyquist sampled echo is a challenging
task. Compressed sensing (CS) has been widely applied in this case to reconstruct the …
task. Compressed sensing (CS) has been widely applied in this case to reconstruct the …
ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold
Tomographic synthetic aperture radar (SAR) technique has attracted remarkable interest for
its ability of 3-D resolving along the elevation direction via a stack of SAR images collected …
its ability of 3-D resolving along the elevation direction via a stack of SAR images collected …
An Iterative Multidimensional Feature Reconstruction Network for Tomographic SAR Imaging
The tomographic Synthetic Aperture Radar (SAR) reconstruction based on deep learning
(DL) can achieve high precision and efficiency, making it a promising method for various …
(DL) can achieve high precision and efficiency, making it a promising method for various …
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 …
[HTML][HTML] Sparse SAR Imaging Based on Non-Local Asymmetric Pixel-Shuffle Blind Spot Network
Y Zhao, D Xiao, Z Pan, BWK Ling, Y Tian, Z Zhang - Remote Sensing, 2024 - mdpi.com
The integration of Synthetic Aperture Radar (SAR) imaging technology with deep neural
networks has experienced significant advancements in recent years. Yet, the scarcity of high …
networks has experienced significant advancements in recent years. Yet, the scarcity of high …