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Journal ArticleDOI

Y-Net: a one-to-two deep learning framework for digital holographic reconstruction.

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TLDR
This Letter proposes a digital holographic reconstruction method with a one-to-two deep learning framework (Y-Net) that can simultaneously reconstruct intensity and phase information from a single digital hologram.
Abstract
In this Letter, for the first time, to the best of our knowledge, we propose a digital holographic reconstruction method with a one-to-two deep learning framework (Y-Net). Perfectly fitting the holographic reconstruction process, the Y-Net can simultaneously reconstruct intensity and phase information from a single digital hologram. As a result, this compact network with reduced parameters brings higher performance than typical network variants. The experimental results of the mouse phagocytes demonstrate the advantages of the proposed Y-Net.

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Citations
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Journal ArticleDOI

Deep learning in optical metrology: a review

TL;DR: Deep learning-enabled optical metrology is a kind of data-driven approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances as discussed by the authors .
Journal ArticleDOI

Deep learning in optical metrology: a review

TL;DR: Deep learning-enabled optical metrology is a kind of data-driven approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances as discussed by the authors .
Journal ArticleDOI

Roadmap on holography

John T. Sheridan, +57 more
- 07 Dec 2020 - 
TL;DR: John T Sheridan, Raymond K Kostuk2, Antonio Fimia Gil, Y Wang4, W Lu4, H Zhong4, Y Tomita5, C Neipp6, J Francés6, S Gallego6, I Pascual6, V Marinova7,8, S-H Lin7, K-Y Hsu7, F Bruder9, S Hansen9, C Manecke9, R Meisenheimer
Journal ArticleDOI

Smart computational light microscopes (SCLMs) of smart computational imaging laboratory (SCILab)

TL;DR: Four smart computational light microscopes (SCLMs) developed by the SCILab of Nanjing University of Science and Technology, China are presented, empowered by advanced computational microscopy techniques, which not only enables multi-modal contrast-enhanced observations for unstained specimens, but also can recover their three-dimensional profiles quantitatively.
References
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Proceedings Article

Adam: A Method for Stochastic Optimization

TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI

Image quality assessment: from error visibility to structural similarity

TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
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A new microscopic principle.

Dennis Gabor
- 01 May 1948 - 
TL;DR: An improvement of the resolution by one decimal wotild require a correction of the objective to four decimals, a practically hopeless task.
Journal ArticleDOI

Direct recording of holograms by a CCD target and numerical reconstruction.

TL;DR: The principle of recording holograms directly on a CCD target is described and a real image of the object is reconstructed from the digitally sampled hologram by means of numerical methods.
Journal ArticleDOI

Digital recording and numerical reconstruction of holograms

TL;DR: The principles and major applications of digital recording and numerical reconstruction of holograms (digital holography) are described, which are applied to measure shape and surface deformation of opaque bodies and refractive index fields within transparent media.
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