Compressed sensing (CS) reconstruction of nonsparse scenes is one of the difficulties in synthetic aperture radar (SAR) imaging technology. Although the conventional CS method with sparse representation has proven applicable for nonsparse SAR reconstruction, its disadvantages are unsatisfactory imaging quality and high computational complexity under downsampling. In this letter, a novel deep learning approach for nonsparse SAR scene reconstruction is proposed based on sparse representation and the iterative shrinkage threshold algorithm (ISTA). Specifically, we first develop a sparse-representation-based imaging model associated with the $\ell _{1}$ sparse regularizer in nonlinear transform domains. Then, the advantages of the recurrent neural network (RNN) and convolutional neural network (CNN) are incorporated into an ISTA-inspired deep unfolded network (DUN) called SR-ISTA-Net, in which all the parameters are layer-varied rather than handcrafted. The experiments verify that the proposed SR-ISTA-Net can provide high-quality reconstruction results under nonsparse scenes while substantially reducing the imaging time.

中文翻译：

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SR-ISTA-Net：基于稀疏表示的 SAR 成像深度学习方法

非稀疏场景的压缩感知（CS）重建是合成孔径雷达（SAR）成像技术的难点之一。尽管具有稀疏表示的传统CS方法已被证明适用于非稀疏SAR重建，但其缺点是成像质量不理想，下采样计算复杂度高。在这封信中，提出了一种基于稀疏表示和迭代收缩阈值算法 (ISTA) 的非稀疏 SAR 场景重建深度学习方法。具体来说，我们首先开发了一个基于稀疏表示的成像模型，与 $\ell _{1}$非线性变换域中的稀疏正则化器。然后，将循环神经网络 (RNN) 和卷积神经网络 (CNN) 的优点结合到一个名为 SR-ISTA-Net 的 ISTA 启发的深度展开网络 (DUN) 中，其中所有参数都是层变化的，而不是手工制作。实验验证了所提出的 SR-ISTA-Net 可以在非稀疏场景下提供高质量的重建结果，同时大大减少了成像时间。