STLS-LADMM-Net: A deep network for SAR autofocus imaging
M Li, J Wu, W Huo, Z Li, J Yang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) can provide high-resolution electromagnetic backscattering
images of the illuminated area, playing a significant role in various applications. However …
images of the illuminated area, playing a significant role in various applications. However …
SAR image reconstruction and autofocus using complex-valued feature prior and deep network implementation
W Huo, M Li, J Wu, Z Li, J Yang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) plays an important role in remote sensing by providing
electromagnetic images of the observation scene. The prior knowledge-based SAR image …
electromagnetic images of the observation scene. The prior knowledge-based SAR image …
SAE-Net: A deep neural network for SAR autofocus
W Pu - IEEE Transactions on Geoscience and Remote …, 2022 - ieeexplore.ieee.org
The sparsity-driven technique is a widely used tool to solve the synthetic aperture radar
(SAR) imaging problem. However, it always encounters sensitivity to motion errors. To solve …
(SAR) imaging problem. However, it always encounters sensitivity to motion errors. To solve …
3-d sar autofocusing with learned sparsity
Inevitable inaccuracies of 3-D synthetic aperture radar (3-D SAR) imaging geometry may
cause undesired blurs in reconstructed images. Recent advances show impressive results …
cause undesired blurs in reconstructed images. Recent advances show impressive results …
AF-AMPNet: A deep learning approach for sparse aperture ISAR imaging and autofocusing
Inverse synthetic aperture radar (ISAR) imaging and autofocusing are challenging under
sparse aperture (SA) conditions. Traditional imaging or autofocusing methods fail to obtain …
sparse aperture (SA) conditions. Traditional imaging or autofocusing methods fail to obtain …
Afnet and PAFnet: fast and accurate SAR autofocus based on deep learning
Autofocus plays a key role in synthetic aperture radar (SAR) imaging, especially for high-
resolution imaging. In the literature, the minimum-entropy-based algorithms (MEA) have …
resolution imaging. In the literature, the minimum-entropy-based algorithms (MEA) have …
SAF-3DNet: Unsupervised AMP-inspired network for 3-D MMW SAR imaging and autofocusing
Z Zhou, S Wei, H Zhang, R Shen… - … on Geoscience and …, 2022 - ieeexplore.ieee.org
The sparse imaging method based on compressed sensing (CS) is widely used in the field
of millimeter-wave (MMW) synthetic aperture radar (SAR) imaging. However, 3-D sparse …
of millimeter-wave (MMW) synthetic aperture radar (SAR) imaging. However, 3-D sparse …
DeepRED Based Sparse SAR Imaging
The integration of deep neural networks into sparse synthetic aperture radar (SAR) imaging
is explored to enhance SAR imaging performance and reduce the system's sampling rate …
is explored to enhance SAR imaging performance and reduce the system's sampling rate …
Fast SAR autofocus based on ensemble convolutional extreme learning machine
Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown
phase errors in SAR data. Uncompensated phase errors can blur the SAR images …
phase errors in SAR data. Uncompensated phase errors can blur the SAR images …
Enhanced One-Bit SAR Imaging Method Using Two-Level Structured Sparsity to Mitigate Adverse Effects of Sign Flips
S Ge, N Jiang, D Feng, S Song, J Wang… - … on Geoscience and …, 2024 - ieeexplore.ieee.org
One-bit synthetic aperture radar (SAR) imaging technology has garnered significant
attention due to its ability to substantially reduce system complexity and data storage …
attention due to its ability to substantially reduce system complexity and data storage …