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- W4285147851 abstract "In the research topic of three-dimensional (3D) SAR imaging, the sparsity-enforcing techniques offer promise in shortening sensing time and improving reconstruction accuracy. However, many of them only explore the sparse prior of 3D SAR images, which leads to biased estimations in cases of non-sparse scenarios. To remedy this problem, we propose a new network with learned low-rank and sparse priors, i.e., LLRS-Net, to obtain improved reconstructions from sparsely sampled 3D SAR echoes. In our scheme, a two-stage reconstruction algorithmic framework (LSRA) is derived based on sparse and low-rank priors. Wherein, the first stage recovers the measurements from their limited observations by exploring the low-rank prior, while the second estimates the final 3D SAR images with a fast-iterative optimization. Theoretically inspired by LRSA, the LLRS-Net is designed into a cascaded network structure. In LLRS-Net, the trainable weights serve as independent variables and control the algorithmic hyper-parameters via regularizing functions, ensuring a well-conditioned updating tendency. By end-to-end training, the network weights are updated automatically under the guidance of a compound loss function constraining both the outputs of two stages. Finally, the methodology is validated on simulations and measured experiments. These results show that the proposed framework outperforms many state-of-the-art imaging algorithms in recovering 3D SAR images from incomplete echo data." @default.
- W4285147851 created "2022-07-14" @default.
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- W4285147851 date "2022-01-01" @default.
- W4285147851 modified "2023-10-16" @default.
- W4285147851 title "3-D SAR Data-Driven Imaging via Learned Low-Rank and Sparse Priors" @default.
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- W4285147851 doi "https://doi.org/10.1109/tgrs.2022.3175486" @default.
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