Deep Physically Parameterized All-in-One Network for Lens-Free Microscopy Imaging

Lens-free microscopy circumvents the conventional trade-off between large field-of-view and high resolution by eliminating bulky optical lenses and relying on computational phase retrieval. However, existing reconstruction methods often face challenges in simultaneously ensuring fidelity, generalization, and efficiency, restricting their practical deployment. To address these limitations, we propose a deep physically parameterized all-in-one network for single-shot phase retrieval in lens-free microscopy. The framework leverages the fractional Fourier transform as a flexible forward model, where the fractional order and noise intensity are served as controllable parameters to characterize diverse diffraction imaging settings. Building on this formulation, we design an all-in-one reconstruction network that integrates an optimization operator, a controllable proximal network, and a controllable squeeze-and-excitation module, while explicitly embedding physical parameters to dynamically adapt the reconstruction process across varying imaging scenarios. Extensive simulations and experimental results demonstrate that the proposed method, using a single pre-trained model, generalizes effectively to diverse diffraction settings and consistently achieves state-of-the-art performance in both reconstruction accuracy and robustness.