Phase and absorbance retrieval in X-ray holographic microscopy under weak illumination using physics-driven neural networks.

X-ray holographic microscopy is a three-dimensional (3D) imaging technique for nanoscale-resolution imaging of morphological features and phase contrast in biological samples and solid-state materials. However, it is challenging to recover phase and absorbance information from shot-noise-limited (SNL) X-ray holograms acquired under weak illumination. In this study, we propose a deep learning model, named MorpHoloNet-X, for single-shot phase and absorbance retrieval from SNL X-ray holograms using a physics-driven neural network. By incorporating physics-based prior knowledge and wave propagation principles into the neural network, MorpHoloNet-X can directly reconstruct 3D complex wavefield, phase, and absorbance distributions in a simulated 3D volume. The performance of the proposed MorpHoloNet-X is validated using synthetic and experimental SNL holograms, and the results are compared with those of conventional methods. The proposed technique would be utilized to reconstruct phase and absorbance information from hard X-ray holograms acquired under rapid acquisition or weak illumination.