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Related Experiment Video

Updated: Oct 12, 2025

Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

Published on: July 5, 2016

Deep learning for digital holography: a review.

Tianjiao Zeng, Yanmin Zhu, Edmund Y Lam

|November 23, 2021

Summary

This summary is machine-generated.

Deep learning significantly enhances digital holography (DH) performance and enables new functionalities. This survey explores recent deep learning advancements and future directions in DH applications.

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Area of Science:

Optics and Photonics
Artificial Intelligence
Digital Imaging

Background:

Digital holography (DH) is an advanced imaging technique.
Deep learning (DL) has shown significant potential in improving DH.
Further advancements are needed to fully leverage DL in DH.

Purpose of the Study:

To survey recent developments in deep learning applications for digital holography.
To summarize relevant deep learning techniques for DH, including benefits and challenges.
To highlight research achievements and provide an outlook on future directions.

Main Methods:

Literature review of deep learning in digital holography.
Categorization of deep learning techniques applied to DH.
Presentation of case studies across various DH applications.

Main Results:

Deep learning has demonstrated substantial progress in DH performance and functionality.
Various DL algorithms offer distinct advantages and face specific challenges in DH.
Case studies illustrate successful applications of DL in DH.

Conclusions:

Deep learning is a transformative technology for digital holography.
Continued research in DL for DH promises expanded capabilities and applications.
Future work should focus on novel DL architectures and integration for DH.