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Opt Express. 2018 Sep 3;26(18):22603-22614. doi: 10.1364/OE.26.022603.

eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction.

Abstract

It is well known that in-line digital holography (DH) makes use of the full pixel count in forming the holographic imaging. But it usually requires phase-shifting or phase retrieval techniques to remove the zero-order and twin-image terms, resulting in the so-called two-step reconstruction process, i.e., phase recovery and focusing. Here, we propose a one-step end-to-end learning-based method for in-line holography reconstruction, namely, the eHoloNet, which can reconstruct the object wavefront directly from a single-shot in-line digital hologram. In addition, the proposed learning-based DH technique has strong robustness to the change of optical path difference between reference beam and object light and does not require the reference beam to be a plane or spherical wave.

PMID:
30184918
DOI:
10.1364/OE.26.022603

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