PhaseNet: A Deep Convolutional Neural Network for Two-Dimensional Phase Unwrapping

计算机科学 卷积神经网络 人工智能 深度学习 像素 相(物质) 模式识别(心理学) 噪音(视频) 算法 人工神经网络 图像(数学) 有机化学 化学
作者
G. E. Spoorthi,Subrahmanyam Gorthi,Rama Krishna Gorthi
出处
期刊:IEEE Signal Processing Letters [Institute of Electrical and Electronics Engineers]
卷期号:26 (1): 54-58 被引量:194
标识
DOI:10.1109/lsp.2018.2879184
摘要

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 π) 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.
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