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Updated: Jul 10, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

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Published on: February 8, 2014

Aberration Estimation for Synthetic Aperture Digital Holographic Microscope Using Deep Neural Network.

Hosung Jeon¹, Minwoo Jung¹, Gunhee Lee¹

¹School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.

Sensors (Basel, Switzerland)

|November 25, 2023

Summary

This summary is machine-generated.

This study introduces deep neural networks (DNNs) to correct aberrations in synthetic aperture digital holographic microscopy (SA-DHM). The novel method enhances image resolution and quality, overcoming limitations of traditional microscopy techniques.

Keywords:

aberration estimation deep neural network holographic microscope synthetic aperture

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Area of Science:

Optical microscopy
Computational imaging
Biophysics

Background:

Digital holographic microscopy (DHM) measures sample optical properties via diffracted beams.
Lagrange invariance in DHM limits spatial bandwidth product (SBP), affecting resolution and field of view.
Synthetic aperture DHM (SA-DHM) aims to overcome SBP limitations but suffers from aberrations.

Purpose of the Study:

To propose a novel approach using deep neural networks (DNNs) for aberration compensation in SA-DHM.
To extend aberration compensation beyond the objective lens's numerical aperture (NA).
To improve the resolution and image quality of SA-DHM.

Main Methods:

Training a DNN using diffraction patterns and Zernike coefficients obtained through a circular aperture.
Estimating aberration coefficients from a partial diffracted beam masked by a circular aperture.
Implementing DNN-based aberration compensation in the illumination beam of SA-DHM.

Main Results:

Effective compensation of aberrations in the illumination beam of SA-DHM.
Demonstrated improvement in the resolution and quality of sample images via simulations.
Successful estimation of aberration coefficients from limited diffracted beam information.

Conclusions:

Deep neural networks offer a powerful tool for aberration correction in SA-DHM.
The proposed method significantly enhances SA-DHM performance, overcoming prior limitations.
This integration promises advancements in microscopy for broader scientific applications.