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Title: Fringe pattern denoising based on deep learning
Authors: Yan, Ketao
Yu, Yingjie
Huang, Chongtian
Sui, Liansheng
Qian, Kemao
Asundi, Anand Krishna
Keywords: Engineering::Mechanical engineering
Issue Date: 2019
Source: Yan, K., Yu, Y., Huang, C., Sui, L., Qian, K. & Asundi, A. K. (2019). Fringe pattern denoising based on deep learning. Optics Communications, 437, 148-152.
Journal: Optics Communications
Abstract: In this paper, deep learning as a novel algorithm is proposed to reduce the noise of the fringe patterns. Usually, the training samples are acquired through experimental acquisition, but these data can be easily obtained by simulations in the proposed algorithm. Thus, the time cost used for the whole training process is greatly reduced. The performance of the proposed algorithm has been demonstrated through the analysis on the simulated and real fringe patterns. It is obvious that the proposed algorithm has a faster calculation speed compared with existing denoising algorithm, and recovers the fringe patterns with high quality. Most importantly, the proposed algorithm may provide a solution to other denoising problems in the field of optics, such as hologram and speckle denoising.
ISSN: 0030-4018
DOI: 10.1016/j.optcom.2018.12.058
Rights: © 2018 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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