基于残差子空间约束的傅里叶偏振显微镜框架
A Residual-Subspace Constraint Framework for Fourier Ptychographic Microscopy
AI总结：
本文提出了一种基于残差子空间约束的傅里叶偏振显微镜框架，通过子空间分解将低秩系统误差与随机噪声分离，从而在不依赖显微镜硬件校准的情况下实现高保真度的相位和振幅恢复。
摘要：
计算光学成像的重建保真度从根本上受到模型-现实差距的限制，即理想化前向模型与物理成像过程之间的不可避免的不一致。传统方法试图通过详尽的系统校准或显式参数估计来弥合这一差距，但这些方法通常计算成本高且容易陷入严重的非凸停滞。本文引入了一种残差子空间约束框架（RSCF）以实现鲁棒的傅里叶偏振显微镜。与将残差视为无结构误差不同，RSCF利用子空间分解将低秩、系统性不匹配从随机噪声解耦，从而隔离出对前向模型不准确性不变的稳定信息流形。通过将此子空间约束嵌入迭代引擎中，该框架能够选择性地抑制误差放大成分，从而在不进行显式硬件校准的情况下实现高保真度的相位和振幅恢复。数值模拟和实验验证表明，RSCF在严重光学像差和LED对准偏差下能够实现优越的收敛加速和伪影抑制。这种信息导向的范式提供了一种通用、模型无关的策略，以增强计算成像多种模态的鲁棒性。
英文摘要：
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.
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