Deep Learning-Based Interference Fringes Detection Using Convolutional Neural Network
The interference fringes of interferometry are the key to reconstruct a three-dimensional topography. But currently the adjustment of the fringes is done by manual, which is time-consuming and lack of quantitative control. Due to the complexity of the fringes, the traditional methods have low recogn...
Main Authors: | Haowei Li, Chunxi Zhang, Ningfang Song, Huipeng Li |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Photonics Journal |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8735894/ |
Similar Items
-
Adaptive DFT-Based Interferometer Fringe Tracking
by: Pedretti Ettore, et al.
Published: (2005-01-01) -
Theoretical Analysis of Interferometer Wave Front Tilt and Fringe Radiant Flux on a Rectangular Photodetector
by: Franz Konstantin Fuss, et al.
Published: (2013-09-01) -
Phase Extraction from Single Interferogram Including Closed-Fringe Using Deep Learning
by: Daichi Kando, et al.
Published: (2019-08-01) -
Under-Sampled Phase Retrieval of Single Interference Fringe Based on Hilbert Transform
by: Yuan Guo, et al.
Published: (2019-01-01) -
Automatic Image-Based Event Detection for Large-N Seismic Arrays Using a Convolutional Neural Network
by: Miłosz Mężyk, et al.
Published: (2021-01-01)