Physics-Aware Style Transfer for Adaptive Holographic Reconstruction
This addresses the problem of ground truth scarcity in holographic imaging for biomedical applications, offering an incremental improvement over existing deep learning methods.
The paper tackled the ill-posed inverse problem of reconstructing objects' complex amplitude from diffraction patterns in inline holographic imaging by introducing a physics-aware style transfer approach that interprets object-to-sensor distance as an implicit style, enabling adaptive learning without high-quality ground truth datasets. It demonstrated applicability by reconstructing the morphology of dynamically flowing red blood cells, highlighting potential for real-time, label-free imaging.
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.