仅用振幅数据训练扩散模型，实现全息相位恢复
Generalizable Holographic Reconstruction via Amplitude-Only Diffusion Priors
- 用仅含振幅的扩散模型，通过分离梯度优化重建复场
- 无需真实相位数据，对不同物体和成像系统均表现鲁棒
- 可直接用于生物组织等复杂结构，适合低成本全息成像
inline 全息中的相位恢复是一个基础但病态的逆问题，因相干成像中振幅与相位存在非线性耦合。本文提出一种即插即用的新方法，利用仅基于物体振幅训练的扩散模型，从衍射强度中同时恢复振幅与相位。采用预测-校正采样框架，并分别计算振幅与相位的似然梯度，使方法在无需真实相位数据训练的情况下实现复场重建。通过大量仿真与实验验证，该方法在多种物体形状、成像系统配置及模态（包括无透镜系统）下均表现出强泛化能力。值得注意的是，仅用简单振幅数据（如聚苯乙烯微球）训练的扩散先验，成功重建出复杂生物组织结构，凸显其高度适应性。该框架为计算成像中的非线性逆问题提供了一种低成本、通用性强的解决方案，为更广泛的相干成像应用奠定基础。
原文摘要 · Abstract (English)
Phase retrieval in inline holography is a fundamental yet ill-posed inverse problem due to the nonlinear coupling between amplitude and phase in coherent imaging. We present a novel off-the-shelf solution that leverages a diffusion model trained solely on object amplitude to recover both amplitude and phase from diffraction intensities. Using a predictor-corrector sampling framework with separate likelihood gradients for amplitude and phase, our method enables complex field reconstruction without requiring ground-truth phase data for training. We validate the proposed approach through extensive simulations and experiments, demonstrating robust generalization across diverse object shapes, imaging system configurations, and modalities, including lensless setups. Notably, a diffusion prior trained on simple amplitude data (e.g., polystyrene beads) successfully reconstructs complex biological tissue structures, highlighting the method's adaptability. This framework provides a cost-effective, generalizable solution for nonlinear inverse problems in computational imaging, and establishes a foundation for broader coherent imaging applications beyond holography.
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