Enhancing Digital Hologram Reconstruction Using Reverse-Attention Loss for Untrained Physics-Driven Deep Learning Models with Uncertain Distance

arXiv:2403.12056v1 Announce Type: new
Abstract: Untrained Physics-based Deep Learning (DL) methods for digital holography have gained significant attention due to their benefits, such as not requiring an annotated training dataset, and providing interpretability since utilizing the governing laws of hologram formation. However, they are sensitive to the hard-to-obtain precise object distance from the imaging plane, posing the $\textit{Autofocusing}$ challenge. Conventional solutions involve reconstructing image stacks for different potential distances and applying focus metrics to select the best results, which apparently …

abstract arxiv attention benefits cs.cv dataset deep learning digital hologram however interpretability laws loss physics physics.optics training type uncertain