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Updated: Sep 30, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

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Published on: February 8, 2014

Towards practical holographic coherent diffraction imaging via maximum likelihood estimation.

David A Barmherzig, Ju Sun

|March 18, 2022

Summary

This summary is machine-generated.

A new maximum likelihood estimation (MLE) framework improves holographic coherent diffraction imaging (HCDI) by enhancing image reconstruction, even with noisy or incomplete data. This robust algorithm is adaptable to various holographic setups facing Poisson shot noise.

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Area of Science:

Optics
Image Reconstruction
Computational Imaging

Background:

Holographic coherent diffraction imaging (HCDI) is crucial for nanoscale imaging.
Existing HCDI algorithms struggle with noisy data and missing low-frequency information.
Poisson shot noise and beamstop occlusion are common challenges in HCDI.

Purpose of the Study:

To develop a novel algorithmic framework for HCDI.
To enhance image reconstruction accuracy and robustness in practical HCDI scenarios.
To create a versatile method applicable to various holographic imaging systems.

Main Methods:

Developed a new algorithmic framework based on maximum likelihood estimation (MLE).
Applied standard numerical optimization algorithms for implementation.
Validated the method across diverse HCDI conditions, including Poisson noise and missing data.

Main Results:

Achieved superior image reconstruction results in HCDI.
Demonstrated high robustness against Poisson shot noise.
Successfully reconstructed images with missing low-frequency data due to beamstop occlusion.
Showcased the framework's applicability to various HCDI setups with fewer constraints.

Conclusions:

The MLE-based framework offers significant improvements for HCDI image reconstruction.
The method is robust and adaptable to common experimental challenges.
The mathematical framework extends to other holographic imaging systems affected by Poisson noise.