Computational de-noising based on deep learning for phase data in digital holographic interferometry
This paper presents a deep-learning-based algorithm dedicated to the processing of speckle noise in phase measurements in digital holographic interferometry. The deep learning architecture is trained with phase fringe patterns including faithful speckle noise, having non-Gaussian statistics and non-...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2020-03-01
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| Series: | APL Photonics |
| Online Access: | http://dx.doi.org/10.1063/1.5140645 |