Dr. ZHANG Yunping, SherrySherryDr. ZHANG YunpingLiu, XihuiXihuiLiuLam, Edmund Y.Edmund Y.Lam2025-08-262025-08-262024Optics Express, 2024, vol. 32(6), pp. 10444-10460.1094-4087http://hdl.handle.net/20.500.11861/24650Open access<jats:p>Among holographic imaging configurations, inline holography excels in its compact design and portability, making it the preferred choice for on-site or field applications with unique imaging requirements. However, effectively holographic reconstruction from a single-shot measurement remains a challenge. While several approaches have been proposed, our novel unsupervised algorithm, the physics-aware diffusion model for digital holographic reconstruction (PadDH), offers distinct advantages. By seamlessly integrating physical information with a pre-trained diffusion model, PadDH overcomes the need for a holographic training dataset and significantly reduces the number of parameters involved. Through comprehensive experiments using both synthetic and experimental data, we validate the capabilities of PadDH in reducing twin-image contamination and generating high-quality reconstructions. Our work represents significant advancements in unsupervised holographic imaging by harnessing the full potential of the pre-trained diffusion prior.</jats:p>enPeer Reviewed Journal Article10.1364/OE.517233