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Journal Article
Learning-based complex field recovery from digital hologram with various depth objects
- Authors
- Yeon-Gyeong Ju, Hyon-Gon Choo, Jae-Hyeung Park
- Issue Date
- 2022-07
- Citation
- Optics Express, v.30, no.15, pp.26149-26168
- ISSN
- 1094-4087
- Publisher
- Optical Society of America (OSA)
- Language
- English
- Type
- Journal Article
- Abstract
- In this paper, we investigate a learning-based complex field recovery technique of an object from its digital hologram. Most of the previous learning-based approaches first propagate the captured hologram to the object plane and then suppress the DC and conjugate noise in the reconstruction. To the contrary, the proposed technique utilizes a deep learning network to extract the object complex field in the hologram plane directly, making it robust to the object depth variations and well suited for three-dimensional objects. Unlike the previous approaches which concentrate on transparent biological samples having near-uniform amplitude, the proposed technique is applied to more general objects which have large amplitude variations. The proposed technique is verified by numerical simulations and optical experiments, demonstrating its feasibility.
- KSP Keywords
- Biological sample, Deep learning network, Digital hologram, Large amplitude, Learning-based, Numerical simulations, Three dimensional(3D), complex field, deep learning(DL), three-dimensional object, uniform amplitude