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Updated: Sep 16, 2025

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
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Uncertainty-aware Fourier ptychography.

Ni Chen1, Yang Wu2, Chao Tan2

  • 1Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China. nichen@eee.hku.hk.

Light, Science & Applications
|July 7, 2025
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Summary
This summary is machine-generated.

Uncertainty-Aware Fourier ptychography (UA-FP) simultaneously corrects system uncertainties for improved holographic imaging. This novel framework enhances reconstruction quality in challenging conditions without complex calibration.

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Area of Science:

  • Optics and Imaging Science
  • Computational Imaging
  • Holography

Background:

  • Fourier ptychography (FP) provides wide field-of-view and high-resolution imaging but is sensitive to system uncertainties.
  • Current methods address uncertainties like misalignment and aberrations separately, failing to tackle interconnected degradations.
  • Challenges include precise numerical modeling, optical aberrations, and data quality limitations in practical FP implementations.

Purpose of the Study:

  • To introduce a comprehensive framework, Uncertainty-Aware FP (UA-FP), for simultaneously addressing multiple system uncertainties in FP.
  • To develop a differentiable forward model incorporating deterministic and stochastic uncertainties as optimizable parameters.
  • To enable robust FP performance without extensive calibration or data collection.

Main Methods:

  • Developed a fully differentiable forward imaging model for FP.
  • Incorporated deterministic uncertainties (misalignment, aberrations) as optimizable parameters.
  • Utilized differentiable optimization with domain-specific priors for stochastic uncertainties (noise, data quality).

Main Results:

  • UA-FP achieved superior reconstruction quality under challenging conditions.
  • Demonstrated robust performance with reduced sub-spectrum overlap requirements.
  • Maintained high-quality reconstructions even with low bit sensor data.

Conclusions:

  • UA-FP offers a unified approach to mitigate interconnected uncertainties in FP.
  • The framework enhances system reconfigurability and broadens FP's applicability in uncontrolled environments.
  • This method advances FP as a robust measurement tool for practical, real-world applications.