![]() Some of these holographic artifacts include twin-image and self-interference artifacts, which are related to missing phase information additional artifacts appear due to the long coherence length/diameter of the illumination source, which creates speckle and background interference from out-of-focus or unwanted objects in the optical path. However, for most practical applications, holographic images cannot match the speckle- and artifact-free image contrast of incoherent bright-field microscopy. Thus, digital holographic microscopy enables the reconstruction of volumetric samples from a single-hologram measurement without any mechanical scanning 1– 6. ![]() 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.ĭigital holographic microscopy encodes the volumetric information of a sample into a single 2D diffraction pattern. 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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