We demonstrate a deep learning-based hologram reconstruction method that achieves bright-field microscopy image contrast in digital holographic microscopy (DHM), which we termed as “bright-field holography”. In bright-field holography, a generative adversarial network was trained to transform a complex-valued DHM reconstruction (obtained without phase-retrieval) into an equivalent image captured by a high-NA bright-field microscope, corresponding to the same sample plane. As a proof-of-concept, we demonstrated snapshot imaging of pollen samples distributed in 3D, digitally matching the contrast and shallow depth-of-field advantages of bright-field microscopy; this enabled us to digitally image a sample volume using bright-field holography without any physical axial scanning.
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