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

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Computer-generated holography using hybrid planar-spherical wave primitives.

Jani Mäkinen, Ugur Gudelek, Lauri Varjo

|May 4, 2026

Summary

This summary is machine-generated.

We developed a hybrid method for synthesizing holograms, improving 3D display realism and reducing computational load. Machine learning accelerates hologram generation for real-time, high-quality immersive experiences.

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Area of Science:

Optics and Photonics
Computer Vision
Computational Imaging

Background:

Holographic near-eye displays provide immersive 3D viewing but face challenges in holographic data encoding.
The accuracy of wave modeling in hologram synthesis is often limited by computational complexity.
Existing methods struggle to balance wave modeling fidelity with efficient computation.

Purpose of the Study:

To introduce a hybrid wave model for accurate hologram synthesis from light fields and depth.
To develop a machine learning framework for accelerating hologram computation.
To enable real-time generation of high-quality, immersive holographic displays.

Main Methods:

A hybrid method combining planar and spherical wave models for hologram synthesis.
Accurate modeling of diffuse and specular reflections using light fields and depth data.
A machine learning framework to generate training data and accelerate computation.

Main Results:

Improved recreation of deep scenes and defocus blur in numerical simulations.
Demonstrated efficient acceleration of hologram synthesis via machine learning.
Successful real-time synthesis of immersive, high-quality holograms.

Conclusions:

The hybrid wave model effectively synthesizes holograms with enhanced accuracy.
Machine learning significantly reduces computational complexity for hologram generation.
The proposed method enables efficient, real-time holographic display creation.