Article ; Online: Learning-based complex field recovery from digital hologram with various depth objects.
2022 Volume 30, Issue 15, Page(s) 26149–26168
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 ... ...
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. |
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Language | English |
Publishing date | 2022-10-10 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 1491859-6 |
ISSN | 1094-4087 ; 1094-4087 |
ISSN (online) | 1094-4087 |
ISSN | 1094-4087 |
DOI | 10.1364/OE.461782 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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