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Computational de-noising based on deep learning for phase data in digital holographic interferometry
APL Photonics ( IF 5.6 ) Pub Date : 2020-03-19 , DOI: 10.1063/1.5140645 Silvio Montresor 1 , Marie Tahon 2 , Antoine Laurent 2 , Pascal Picart 1, 3
APL Photonics ( IF 5.6 ) Pub Date : 2020-03-19 , DOI: 10.1063/1.5140645 Silvio Montresor 1 , Marie Tahon 2 , Antoine Laurent 2 , Pascal Picart 1, 3
Affiliation
This paper presents a deep-learning-based algorithm dedicated to the processing of speckle noise in phase measurements in digital holographic interferometry. The deep learning architecture is trained with phase fringe patterns including faithful speckle noise, having non-Gaussian statistics and non-stationary property, and exhibiting spatial correlation length. The performances of the speckle de-noiser are estimated with metrics, and the proposed approach exhibits state-of-the-art results. In order to train the network to de-noise phase fringe patterns, a database is constituted with a set of noise-free and speckled phase data. The algorithm is applied to de-noising experimental data from wide-field digital holographic vibrometry. Comparison with the state-of-the-art algorithm confirms the achieved performance.
中文翻译:
基于深度学习的数字全息干涉术中基于相位数据的计算降噪
本文提出了一种基于深度学习的算法,专用于数字全息干涉术中相位测量中斑点噪声的处理。深度学习架构采用相条纹模式进行训练,这些相条纹模式包括忠实的斑点噪声,具有非高斯统计量和非平稳性,并表现出空间相关长度。散斑消噪器的性能通过度量进行估计,并且所提出的方法展现了最新的结果。为了训练网络以消除相位条纹图案的噪声,数据库由一组无噪声且有斑点的相位数据组成。该算法适用于对来自广域数字全息振动计的实验数据进行消噪。与最新算法的比较证实了所获得的性能。
更新日期:2020-04-23
中文翻译:
基于深度学习的数字全息干涉术中基于相位数据的计算降噪
本文提出了一种基于深度学习的算法,专用于数字全息干涉术中相位测量中斑点噪声的处理。深度学习架构采用相条纹模式进行训练,这些相条纹模式包括忠实的斑点噪声,具有非高斯统计量和非平稳性,并表现出空间相关长度。散斑消噪器的性能通过度量进行估计,并且所提出的方法展现了最新的结果。为了训练网络以消除相位条纹图案的噪声,数据库由一组无噪声且有斑点的相位数据组成。该算法适用于对来自广域数字全息振动计的实验数据进行消噪。与最新算法的比较证实了所获得的性能。