PhaseNet: A Deep Convolutional Neural Network for Two-Dimensional Phase Unwrapping
PhaseNet: A Deep Convolutional Neural Network for Two-Dimensional Phase Unwrapping
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DOI：
10.1109/lsp.2018.2879184
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发表时间：
2019-01-01
影响因子：
3.9
通讯作者：
Gorthi, Rama Krishna Sai Subrahmanyam
中科院分区：
文献类型：
--
作者：
Spoorthi, G. E.;Gorthi, Subrahmanyam;Gorthi, Rama Krishna Sai Subrahmanyam
相位展开是多种应用中的关键信号处理问题，旨在从包裹相位恢复原始相位。在这封信中，我们提出了一种使用深度全卷积神经网络（称为 PhaseNet）来展开相位的新颖框架。我们将直接获得连续原始相位的问题定义重新定义为通过语义分割获得每个像素的环绕计数（2 pi 的整数跳跃），这是通过合适的深度学习框架来完成的。所提出的架构由编码器网络、相应的解码器网络和像素级分类层组成。利用绝对相位和环绕计数之间的关系来生成多个随机形状的丰富模拟数据。这使网络在包裹的相位图中学习连续性，而不是训练数据中的特定模式。我们将所提出的框架与广泛采用的质量引导相位展开算法以及著名的 MATLAB 针对不同噪声水平的展开函数进行了比较。我们发现所提出的框架对噪声具有鲁棒性并且计算速度快。获得的结果凸显了深度卷积神经网络确实可以有效地应用于相位展开，并且所提出的框架有望为开发一套新的基于深度学习的相位展开方法铺平道路。
Phase unwrapping is a crucial signal processing problem in several applications that aims to restore original phase from the wrapped phase. In this letter, we propose a novel framework for unwrapping the phase using deep fully convolutional neural network termed as PhaseNet. We reformulate the problem definition of directly obtaining continuous original phase as obtaining the wrap-count (integer jump of 2 pi) at each pixel by semantic segmentation and this is accomplished through a suitable deep learning framework. The proposed architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The relationship between the absolute phase and the wrap-count is leveraged in generating abundant simulated data of several random shapes. This deliberates the network on learning continuity in wrapped phase maps rather than specific patterns in the training data. We compare the proposed framework with the widely adapted quality-guided phase unwrapping algorithm and also with the well-known MATLAB's unwrap function for varying noise levels. The proposed framework is found to be robust to noise and computationally fast. The results obtained highlight that deep convolutional neural network can indeed be effectively applied for phase unwrapping, and the proposed framework will hopefully pave the way for the development of a new set of deep learning based phase unwrapping methods.