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|a 10.1109/TIP.2021.3104168
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|a eng
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|a Wang, Mou
|e verfasserin
|4 aut
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|a TPSSI-Net
|b Fast and Enhanced Two-Path Iterative Network for 3D SAR Sparse Imaging
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|c 2021
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|a Text
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|a Date Revised 23.08.2021
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a 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 and improve image quality. However, the conventional CS-driven algorithms are always limited by huge computational costs and non-trivial tuning of parameters. In this article, to address this problem, we propose a two-path iterative framework dubbed TPSSI-Net for 3D SAR sparse imaging. By mapping the AMP into a layer-fixed deep neural network, each layer of TPSSI-Net consists of four modules in cascade corresponding to four steps of the AMP optimization. Differently, the Onsager terms in TPSSI-Net are modified to be differentiable and scaled by learnable coefficients. Rather than manually choosing a sparsifying basis, a two-path convolutional neural network (CNN) is developed and embedded in TPSSI-Net for nonlinear sparse representation in the complex-valued domain. All parameters are layer-varied and optimized by end-to-end training based on a channel-wise loss function, bounding both symmetry constraint and measurement fidelity. Finally, extensive SAR imaging experiments, including simulations and real-measured tests, demonstrate the effectiveness and high efficiency of the proposed TPSSI-Net
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|a Journal Article
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|a Wei, Shunjun
|e verfasserin
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| 700 |
1 |
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|a Liang, Jiadian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Zichen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Qu, Qizhe
|e verfasserin
|4 aut
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| 700 |
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|a Shi, Jun
|e verfasserin
|4 aut
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| 700 |
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|a Zhang, Xiaoling
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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|g 30(2021) vom: 04., Seite 7317-7332
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|u http://dx.doi.org/10.1109/TIP.2021.3104168
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