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

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.