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|Title:||Fringe pattern denoising based on deep learning||Authors:||Yan, Ketao
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. https://dx.doi.org/10.1016/j.optcom.2018.12.058||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.||URI:||https://hdl.handle.net/10356/151313||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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