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Digital hologram for data augmentation in learning-based pattern classification
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DOI：10.1364/OL.43.005419.png)
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This study proposes a novel data augmentation method based on numerical focusing of digital holography to boost the performance of learning-based pattern classification. To conduct digital holographic data augmentation (DHDA), a complex pattern diffraction approach is used to provide the least separation of confusion in the effective diffraction regime to access the full-field wavefront information of a target sample. By using DHDA, the accessible amount of labeled data is increased to complement the data manifold and to provide various three-dimensional diffraction characteristics for improving the performance of learning-based pattern classification. Experimental results demonstrated that overall accuracy of pattern classification with DHDA (95.1%) was higher than that without DHDA (90.9%). (C) 2018 Optical Society of America
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MICROSCOPY
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IF:
3.3
论文数:
4.0W
被引数:
7.6W
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