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Hybrid Iterating-Averaging Low Photon Budget Gabor Holographic Microscopy.

Mikolaj Rogalski1, Piotr Arcab1, Emilia Wdowiak1

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Summary

A new iterative Gabor averaging (IGA) algorithm enhances label-free live cell imaging by reducing noise in low illumination conditions. This method improves quantitative phase imaging (QPI) for dynamic biological samples and optically thin specimens.

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Area of Science:

  • Biophysics
  • Optical Microscopy
  • Image Processing

Background:

  • Quantitative phase imaging (QPI) faces challenges in achieving high-contrast, label-free imaging with minimal impact on live cells.
  • Low photon budget (LPB) imaging reduces phototoxicity but introduces significant camera shot noise and quantification noise, hindering QPI.
  • Digital in-line holographic microscopy (DIHM) offers robustness for LPB data, yet struggles with simultaneous twin image and shot noise suppression.

Purpose of the Study:

  • To develop a novel algorithm for effective noise reduction in multiframe DIHM under low illumination intensities.
  • To address the critical challenge of simultaneously minimizing twin image perturbation and camera shot noise in DIHM.
  • To enable high-speed, photostimulation-free imaging of dynamic biological samples and optically thin specimens.

Main Methods:

  • Introduction of the iterative Gabor averaging (IGA) algorithm, combining iterative phase retrieval with frame averaging.
  • Leveraging an iterative process to reconstruct high-fidelity phase images while averaging camera shot noise across frames.
  • Utilizing multiframe DIHM data acquired under low illumination conditions.

Main Results:

  • Simulations demonstrated that IGA outperforms conventional methods in reconstruction accuracy, especially under high-noise conditions.
  • Experimental validation showed IGA's efficacy in high-speed imaging of dynamic sperm cells and static phase targets under low illumination.
  • The algorithm proved successful for optically thin samples, which typically produce low signal-to-noise holograms.

Conclusions:

  • The IGA algorithm effectively suppresses twin image disturbance and shot noise in multiframe DIHM, enabling high-quality imaging under low photon budgets.
  • IGA is a powerful tool for photostimulation-free, high-speed imaging of dynamic biological samples.
  • This advancement enhances imaging capabilities for samples with extremely low optical thickness, with potential biomedical and environmental applications in low-light settings.