Unrolled Wirtinger Flow With Deep Decoding Priors for Phaseless Imaging
Unrolled Wirtinger Flow With Deep Decoding Priors for Phaseless Imaging
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DOI：
10.1109/tci.2022.3189217
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发表时间：
2021-08
影响因子：
5.4
通讯作者：
Samia Kazemi;Bariscan Yonel;B. Yazıcı
中科院分区：
文献类型：
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作者：
Samia Kazemi;Bariscan Yonel;B. Yazıcı
我们介绍了基于深度学习（DL）的网络和相关的精确恢复理论，用于仅强度测量的成像。网络架构使用循环结构展开Wirtinger Flow （WF）算法，该算法具有深度解码先验，能够在较低维编码图像空间中执行算法更新。我们使用一个单独的深度网络（DN），称为编码网络，用于将WF算法中使用的频谱初始化转换为编码域的适当初始值。展开方案将底层优化算法的固定迭代次数建模为循环神经网络（RNN）。此外，它有助于同时学习解码和编码网络和RNN的参数。建立了在确定性正演模型下保证精确回收的充分条件。此外，我们还证明了训练后的解码先验和编码网络的Lipschitz常数与WF算法的收敛速率之间的关系。利用PCSWAT软件的高保真仿真数据，证明了该方法在合成孔径成像中的实际适用性。我们的数值研究表明，解码先验和编码网络有助于提高样本复杂度。
We introduce a deep learning (DL) based network and an associated exact recovery theory for imaging from intensity-only measurements. The network architecture uses a recurrent structure that unrolls the Wirtinger Flow (WF) algorithm with a deep decoding prior that enables performing the algorithm updates in a lower dimensional encoded image space. We use a separate deep network (DN), referred to as the encoding network, for transforming the spectral initialization used in the WF algorithm to an appropriate initial value for the encoded domain. The unrolling scheme models a fixed number of iterations of the underlying optimization algorithm into a recurrent neural network (RNN). Furthermore, it facilitates simultaneous learning of the parameters of the decoding and encoding networks and the RNN. We establish a sufficient condition to guarantee exact recovery under deterministic forward models. Additionally, we demonstrate the relation between the Lipschitz constants of the trained decoding prior and encoding networks to the convergence rate of the WF algorithm. We show the practical applicability of our method in synthetic aperture imaging using high fidelity simulation data from the PCSWAT software. Our numerical study shows that the decoding prior and the encoding network facilitate improvements in sample complexity.