CTV-Net: Complex-valued TV-driven network with nested topology for 3-D SAR imaging
The regularization-based approaches offer promise in improving synthetic aperture radar
(SAR) imaging quality while reducing system complexity. However, the widely applied …
(SAR) imaging quality while reducing system complexity. However, the widely applied …
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 …
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 …
SPB-Net: A deep network for SAR imaging and despeckling with downsampled data
K Xiong, G Zhao, Y Wang, G Shi - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) typically faces both large-scale data and speckle noise
problems, which, respectively, induce enormous strains on transmission and storage and …
problems, which, respectively, induce enormous strains on transmission and storage and …
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 …
Lq-SPB-Net: A Real-Time Deep Network for SAR Imaging and Despeckling
K Xiong, G Zhao, Y Wang, G Shi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Large quantities of sampling data and speckle noise are two serious problems existing in
synthetic aperture radar (SAR). The former puts enormous strain on data measurement …
synthetic aperture radar (SAR). The former puts enormous strain on data measurement …
A model-data-driven network embedding multidimensional features for tomographic SAR imaging
Deep learning (DL)-based tomographic SAR im-aging algorithms are gradually being
studied. Typically, they use an unfolding network to mimic the iterative calculation of the …
studied. Typically, they use an unfolding network to mimic the iterative calculation of 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 …
Learning-based split unfolding framework for 3-D mmW radar sparse imaging
The application of the compressed sensing (CS) method in the radar field enables the radar
imaging system to satisfy both low data cost and high reconstruction quality; however, it is …
imaging system to satisfy both low data cost and high reconstruction quality; however, it is …
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 …