Digital hologram for data augmentation in learning-based pattern classification

Chau Jern Cheng, Kuang Che Chang Chien, Yu Chih Lin

Research output: Contribution to journalArticle

2 Citations (Scopus)


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 languageEnglish
Pages (from-to)5419-5422
Number of pages4
JournalOptics Letters
Issue number21
Publication statusPublished - 2018 Nov 1


ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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