%0 Journal Article
%T Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram
%J -
%D 2019
%R https://doi.org/10.1038/s41377-019-0139-9
%X Digital holographic microscopy enables the 3D reconstruction of volumetric samples from a single-snapshot hologram. However, unlike a conventional bright-field microscopy image, the quality of holographic reconstructions is compromised by interference fringes as a result of twin images and out-of-plane objects. Here, we demonstrate that cross-modality deep learning using a generative adversarial network (GAN) can endow holographic images of a sample volume with bright-field microscopy contrast, combining the volumetric imaging capability of holography with the speckle- and artifact-free image contrast of incoherent bright-field microscopy. We illustrate the performance of this ˇ°bright-field holographyˇ± method through the snapshot imaging of bioaerosols distributed in 3D, matching the artifact-free image contrast and axial sectioning performance of a high-NA bright-field microscope. This data-driven deep-learning-based imaging method bridges the contrast gap between coherent and incoherent imaging, and enables the snapshot 3D imaging of objects with bright-field contrast from a single hologram, benefiting from the wave-propagation framework of holography
%U https://www.nature.com/articles/s41377-019-0139-9