Model-based deep learning approach for dynamic holographic reconstruction

Non-interferometric holographic reconstruction is an ill-posed inverse problem that is faced with an inherent tradeoff between temporal resolution and image quality. Here, we propose a model-based deep learning framework for dynamic holographic reconstruction. By exploiting the deep spatiotemporal priors learned from large-scale video datasets, we realized time-resolved holographic imaging of dynamic samples with framerate-limited temporal resolution.