![]() Maskless imaging of dense samples using pixel super-resolution based multi-height lensfree on-chip microscopy. Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications. Automatic focusing in digital holography and its application to stretched holograms. Endowing a plain fluidic chip with micro-optics: a holographic microscope slide. Lensless digital holographic microscopy and its applications in biomedicine and environmental monitoring. Imaging without lenses: achievements and remaining challenges of wide-field on-chip microscopy. Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy. Digital in-line holography for biological applications. 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. We illustrate the performance of this "bright-field holography" 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. 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. 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. Digital holographic microscopy enables the 3D reconstruction of volumetric samples from a single-snapshot hologram.
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