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Yixuan Li
Researcher at Nanjing University of Science and Technology
Publications - 28
Citations - 626
Yixuan Li is an academic researcher from Nanjing University of Science and Technology. The author has contributed to research in topics: Computer science & Absolute phase. The author has an hindex of 2, co-authored 10 publications receiving 179 citations.
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Journal ArticleDOI
Deep learning in optical metrology: a review
Chao Zuo,Jiaming Qian,Shijie Feng,Wei Yin,Yixuan Li,Pengfei Fan,Jing Han,Kemao Qian,Qian Chen +8 more
TL;DR: Deep learning-enabled optical metrology is a kind of data-driven approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances as discussed by the authors .
Journal ArticleDOI
Single-shot absolute 3D shape measurement with deep-learning-based color fringe projection profilometry
TL;DR: Inspired by recent successes of deep learning for FPP, this work proposes a single-shot absolute 3D shape measurement with deep-learning-based color FPP that allows for more accurate phase retrieval and more robust phase unwrapping.
Journal ArticleDOI
Deep-learning-enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement
TL;DR: Zhang et al. as mentioned in this paper proposed a deep learning framework for phase analysis based on stereo phase unwrapping (SPU), which can eliminate phase ambiguity without projecting any additional patterns, which maximizes the efficiency of the retrieval of the absolute phase.
Journal ArticleDOI
Deep learning in optical metrology: a review
Chao Zuo,Jiaming Qian,Shijie Feng,Wei Yin,Yixuan Li,Pengfei Fan,Jing Han,Kemao Qian,Qian Chen +8 more
TL;DR: Deep learning-enabled optical metrology is a kind of data-driven approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances as discussed by the authors .
Journal ArticleDOI
Deep-learning-enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement
TL;DR: It is demonstrated that deep learning can be an effective tool that organically unifies the phase retrieval, geometric constraints, and phase unwrapping steps into a comprehensive framework and represents an important step forward in high-speed, high-accuracy, motion-artifacts-free absolute 3D shape measurement for complicated object from a single fringe pattern.