Uncertainty-aware Fourier ptychography

Ni Chen; Yang Wu; Chao Tan; Liangcai Cao; Jun Wang; Edmund Y. Lam

doi:10.1038/s41377-025-01915-w

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Uncertainty-aware Fourier ptychography

Original Articles

- Uncertainty-aware Fourier ptychography
- Light: Science & Applications Vol. 14, Issue 9, Pages: 2514-2529(2025)
- Affiliations：
  
  1.Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
  2.School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
  3.Department of Precision Instruments, Tsinghua University, 100084 Beijing, China
- Author bio：
  
  Ni Chen (nichen@eee.hku.hk)
  Jun Wang (jwang@scu.edu.cn)
  Edmund Y. Lam (elam@eee.hku.hk)
- Funds：
- DOI：10.1038/s41377-025-01915-w
  CLC：
- Received：15 October 2024，
  
  Revised：2025-05-24，
  
  Accepted：31 May 2025，
  
  Published Online：07 July 2025，
  
  Published：30 September 2025
- Accepted：
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Chen, N. et al. Uncertainty-aware Fourier ptychography. Light: Science & Applications, 14, 2514-2529 (2025).
DOI：

Chen, N. et al. Uncertainty-aware Fourier ptychography. Light: Science & Applications, 14, 2514-2529 (2025). DOI： 10.1038/s41377-025-01915-w.

摘要

Abstract

Fourier ptychography (FP) offers both wide field-of-view and high-resolution holographic imaging

making it valuable for applications ranging from microscopy and X-ray imaging to remote sensing. However

its practical implementation remains challenging due to the requirement for precise numerical forward models that accurately represent real-world imaging systems. This sensitivity to model-reality mismatches makes FP vulnerable to physical uncertainties

including misalignment

optical element aberrations

and data quality limitations. Conventional approaches address these challenges through separate methods: manual calibration or digital correction for misalignment; pupil or probe reconstruction to mitigate aberrations; or data quality enhancement through exposure adjustments or high dynamic range (HDR) techniques. Critically

these methods cannot simultaneously address the interconnected uncertainties that collectively degrade imaging performance. We introduce Uncertainty-Aware FP (UA-FP)

a comprehensive framework that simultaneously addresses multiple system uncertainties without requiring complex calibration and data collection procedures. Our approach develops a fully differentiable forward imaging model that incorporates deterministic uncertainties (misalignment and optical aberrations) as optimizable parameters

while leveraging differentiable optimization with domain-specific priors to address stochastic uncertainties (noise and data quality limitations). Experimental results demonstrate that UA-FP achieves superior reconstruction quality under challenging conditions. The method maintains robust performance with reduced sub-spectrum overlap requirements and retains high-quality reconstructions even with low bit sensor data. Beyond improving image reconstruction

our approach enhances system reconfigurability and extends FP's capabilities as a measurement tool suitable for operation in environments where precise alignment and calibration are impractical.

关键词

Keywords

references

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