论文标题
联合卷积和空间四向LSTM网络，用于拆卸
A Joint Convolutional and Spatial Quad-Directional LSTM Network for Phase Unwrapping
论文作者
论文摘要
阶段解析是一个经典不足的问题，旨在从包装阶段恢复真实阶段。在本文中，我们介绍了一种新型的卷积神经网络（CNN），该神经网络（CNN）结合了空间四向长期记忆（SQD-LSTM），以进行相位解析，并通过将其作为回归问题提出。合并SQD-LSTM可以规避典型的CNN学习全局空间依赖性的固有难度，这些困难在恢复真实阶段时至关重要。此外，我们采用特定问题的综合损失函数来训练该网络。在严重的噪声条件下（SNR = 0 dB时归一化的均方根误差为1.3％），发现所提出的网络的性能要比现有方法更好，同时花费明显较小的计算时间（0.054 s）。该网络在培训过程中也不需要大规模数据集，因此非常适合具有有限数据的应用程序，需要快速，准确的阶段解开。
Phase unwrapping is a classical ill-posed problem which aims to recover the true phase from wrapped phase. In this paper, we introduce a novel Convolutional Neural Network (CNN) that incorporates a Spatial Quad-Directional Long Short Term Memory (SQD-LSTM) for phase unwrapping, by formulating it as a regression problem. Incorporating SQD-LSTM can circumvent the typical CNNs' inherent difficulty of learning global spatial dependencies which are vital when recovering the true phase. Furthermore, we employ a problem specific composite loss function to train this network. The proposed network is found to be performing better than the existing methods under severe noise conditions (Normalized Root Mean Square Error of 1.3 % at SNR = 0 dB) while spending a significantly less computational time (0.054 s). The network also does not require a large scale dataset during training, thus making it ideal for applications with limited data that require fast and accurate phase unwrapping.
