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.

中文翻译：

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TPSSI-Net：用于 3D SAR 稀疏成像的快速增强型双路径迭代网络

结合压缩感知 (CS) 和三维合成孔径雷达 (3D SAR) 成像的新兴领域已显示出降低采样率和提高图像质量的巨大潜力。然而，传统的 CS 驱动算法总是受到巨大的计算成本和参数的非平凡调整的限制。在本文中，为了解决这个问题，我们提出了一个名为 TPSSI-Net 的双路径迭代框架，用于 3D SAR 稀疏成像。通过将 AMP 映射到固定层的深度神经网络，TPSSI-Net 的每一层由四个级联的模块组成，对应于 AMP 优化的四个步骤。不同的是，TPSSI-Net 中的 Onsager 项被修改为可微分并通过可学习系数进行缩放。而不是手动选择一个稀疏的基础，开发了一个双路径卷积神经网络 (CNN) 并将其嵌入到 TPSSI-Net 中，用于复值域中的非线性稀疏表示。所有参数都是分层变化的，并通过基于通道损失函数的端到端训练进行优化，限制了对称性约束和测量保真度。最后，大量的 SAR 成像实验，包括模拟和实测测试，证明了所提出的 TPSSI-Net 的有效性和高效率。