Physics-aware style transfer for adaptive holographic reconstruction

Inline holographic imaging presents an ill-posed inverse problem of reconstructing the complex amplitude of the scattered wavefield, which is governed by the object’s complex refractive index, 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, a physics-aware style transfer framework is proposed that treats 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, inverse mapping can be learned in an adaptive manner only with datasets composed of intensity measurements. Biomedical applicability is demonstrated 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.