Constraint-Free Coherent Diffraction Imaging via Physics-Guided Neural Fields

TL;DR

This paper introduces CDIP, a constraint-free neural network framework for coherent diffraction imaging that improves reconstruction fidelity and stability without relying on handcrafted constraints, applicable across various imaging modalities.

Contribution

It presents a novel physics-guided neural field approach that eliminates the need for object-specific constraints in phase retrieval for CDI, enhancing robustness and applicability.

Findings

Outperforms classical iterative algorithms in fidelity and stability

Effective on both simulated and experimental datasets

Applicable to static and dynamic CDI reconstructions

Abstract

CDI is a lensless imaging technique that enables atomic-resolution imaging of non-crystalline specimens and their dynamics. However, its broader implementation has been hindered by the instability and ill-posedness of its reconstruction process, known as phase retrieval, which relies heavily on handcrafted, object-specific constraints. To overcome the key limitations, we propose CDIP, a robust phase-retrieval framework that eliminates the need for such constraints by combining untrained coordinate-based neural fields for static and dynamic reconstructions and a physics-consistent forward model. We evaluate CDIP on simulated and experimental datasets that involve both static samples and dynamic processes, demonstrating that it substantially outperforms classical iterative algorithms and deep-learning baselines in terms of fidelity and stability. These results highlight a paradigm shift in both static and time-resolved CDI reconstruction, providing a broadly applicable framework for coherent imaging modalities such as ptychography and holography, across X-ray, electron, and optical probes.