Physics-Aware Style Transfer for Adaptive Holographic Reconstruction
Journal:
arXiv
Published Date:
Jul 1, 2025
Abstract
Inline holographic imaging presents an ill-posed inverse problem of
reconstructing objects' complex amplitude from recorded diffraction patterns.
Although recent deep learning approaches have shown promise over classical
phase retrieval algorithms, they often require high-quality ground truth
datasets of complex amplitude maps to achieve a statistical inverse mapping
operation between the two domains. Here, we present a physics-aware style
transfer approach that interprets the object-to-sensor distance as an implicit
style within diffraction patterns. Using the style domain as the intermediate
domain to construct cyclic image translation, we show that the inverse mapping
operation can be learned in an adaptive manner only with datasets composed of
intensity measurements. We further demonstrate its biomedical applicability by
reconstructing the morphology of dynamically flowing red blood cells,
highlighting its potential for real-time, label-free imaging. As a framework
that leverages physical cues inherently embedded in measurements, the presented
method offers a practical learning strategy for imaging applications where
ground truth is difficult or impossible to obtain.
