상세정보
학술지
Learning-based complex field recovery from digital hologram with various depth objects
- 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)
- 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