标题:Optical fringe patterns filtering based on multi-stage convolution neural network
作者:Lin, Bowen; Fu, Shujun; Zhang, Caiming; Wang, Fengling; Li, Yuliang
作者机构:[Lin, Bowen; Fu, Shujun] Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China.; [Wang, Fengling] Shandong Univ Arts, Coll Arts Managemen 更多
通讯作者:Fu, Shujun;Fu, SJ
通讯作者地址:[Fu, SJ]Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China.
来源:OPTICS AND LASERS IN ENGINEERING
出版年:2020
卷:126
DOI:10.1016/j.optlaseng.2019.105853
关键词:Fringe patterns denoising; Image restoration; Regularization;; Convolution neural network; Leaky relu
摘要:Optical fringe patterns are often contaminated by speckle noise, making it difficult to accurately and robustly extract their phase fields. To deal with this problem, we propose a filtering method based on deep learning, called optical fringe patterns denoising convolutional neural network (FPD-CNN), for directly removing speckle from the input noisy fringe patterns. Regularization technology is integrated into the design of deep architecture. Specifically, the FPD-CNN method is divided into multiple stages, each stage consists of a set of convolutional layers along with batch normalization and leaky rectified linear unit (Leaky ReLU) activation function. The end-to-end joint training is carried out using the Euclidean loss. Extensive experiments on simulated and experimental optical fringe patterns, especially finer ones with high-density regions, show that the proposed method is competitive with some state-of-the-art denoising techniques in spatial or transform domains, efficiently preserving main features of fringe at a fairly fast speed.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073620256&doi=10.1016%2fj.optlaseng.2019.105853&partnerID=40&md5=1ac43604b38383df156aab6569a2e6b0
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