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학술지 Learning-based complex field recovery from digital hologram with various depth objects
Cited 9 time in scopus Download 106 time Share share facebook twitter linkedin kakaostory
저자
주연경, 추현곤, 박재형
발행일
202207
출처
Optics Express, v.30 no.15, pp.26149-26168
ISSN
1094-4087
출판사
Optical Society of America (OSA)
DOI
https://dx.doi.org/10.1364/OE.461782
협약과제
22HH5700, 홀로그램 기반의 위상 검출용 디지털 홀로그래피 메트롤로지 기술 개발, 임용준
초록
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 제안 키워드
Biological sample, Deep learning network, Digital hologram, Large amplitude, Learning-based, Numerical simulations, Three dimensional(3D), Three-dimensional Objects, complex field, deep learning(DL), uniform amplitude