近日，四川大学王君团队研究了不确定性感知傅立叶平面印刷术。相关论文于2025年7月7日发表在《光：科学与应用》杂志上。

傅立叶平面摄影（FP）提供宽视场和高分辨率全息成像，使其在从显微镜和x射线成像到遥感的应用中具有价值。然而，由于需要精确的数值正演模型来准确地代表现实世界的成像系统，因此其实际实施仍然具有挑战性。这种对模型与现实不匹配的敏感性使得FP容易受到物理不确定性的影响，包括不对准、光学元件像差和数据质量限制。传统方法通过不同的方法解决这些挑战：手动校准或数字校正不对准；瞳孔或探头重建以减轻像差；或通过曝光调整或高动态范围（HDR）技术来提高数据质量。关键的是，这些方法不能同时解决相互关联的不确定性，这些不确定性共同降低了成像性能。

研究组介绍了不确定性感知FP (UA-FP)，这是一个综合框架，可以同时解决多个系统不确定性，而不需要复杂的校准和数据收集程序。该方法开发了一种完全可微的正演成像模型，该模型将确定性不确定性（不对准和光学像差）作为可优化参数，同时利用特定领域先验的可微优化来解决随机不确定性（噪声和数据质量限制）。实验结果表明，在具有挑战性的条件下，UA-FP具有较好的重建质量。该方法在降低子频谱重叠要求的同时保持了机器人性能，即使在低比特传感器数据下也能保持高质量的重建。除了改善图像重建，他们的方法增强了系统的可重构性，并扩展了FP作为测量工具的能力，适合在精确对准和校准不切实际的环境中操作。

附：英文原文

Title: Uncertainty-aware Fourier ptychography

Author: Chen, Ni, Wu, Yang, Tan, Chao, Cao, Liangcai, Wang, Jun, Lam, Edmund Y.

Issue&Volume: 2025-07-07

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.

DOI: 10.1038/s41377-025-01915-w

Source: https://www.nature.com/articles/s41377-025-01915-w