Abstract
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%).
Original language | English |
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Pages (from-to) | 5419-5422 |
Number of pages | 4 |
Journal | Optics Letters |
Volume | 43 |
Issue number | 21 |
DOIs | |
Publication status | Published - 2018 Nov 1 |
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics