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 impl...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.展开更多
基金supported by the Hong Kong Research Grants Council(GRF 17200321,GRF 17201822)Y.W.and J.W.work was supported by the National Natural Science Foundation of China(62275178).
文摘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.
