A Residual-Subspace Constraint Framework for Fourier Ptychographic Microscopy

TL;DR

The paper proposes a novel Residual-Subspace Constraint Framework (RSCF) for Fourier ptychographic microscopy that improves reconstruction robustness and speed by decoupling systematic errors from noise without requiring hardware calibration.

Contribution

It introduces a subspace decomposition approach to enhance robustness and convergence in Fourier ptychographic microscopy without explicit hardware calibration.

Findings

RSCF achieves faster convergence and better artifact suppression.

Numerical and experimental results validate improved robustness under optical aberrations.

The framework is versatile and applicable across various computational imaging modalities.

Abstract

The reconstruction fidelity of computational optical imaging is fundamentally constrained by the model-reality gap, i.e., the inevitable discrepancy between idealized forward models and the physical imaging process. Conventional paradigms attempt to bridge this gap through exhaustive system calibration or explicit parameter estimation, which are often computationally intensive and prone to severe non-convex stagnation. This paper introduces a Residual-Subspace Constraint Framework (RSCF) to achieve robust Fourier ptychographic microscopy. Instead of treating residuals as unstructured errors, RSCF leverages subspace decomposition to decouple low-rank, systematic mismatches from stochastic noise, thereby isolating stable information manifolds that remain invariant to forward-model inaccuracies. By embedding this subspace constraint into the iterative engine, the framework selectively suppresses error-amplifying components, enabling high-fidelity phase and amplitude recovery without explicit hardware calibration. Numerical simulations and experimental validations demonstrate that RSCF yields superior convergence acceleration and artifact suppression under severe optical aberrations and LED misalignment. This information-centric paradigm provides a versatile, model-agnostic strategy to enhance robustness across diverse computational imaging modalities.