Digital Holographic Microscopy (DHM) has been a successful imaging technique for various applications in biomedical imaging, particle analysis, and optical engineering. Though DHM has been successful in reconstructing 3D volumes with stationary objects, it has still been a challenging task to track fast mobile objects. Recent advancements in deep learning with convolutional neural networks have been proven useful in solving experimental difficulties, starting from tracking single particles to multiple bacterial cells. Here, we propose a compact DHM driven by neural networks with a minimal amount of optical elements with an ultimate aim for easy usage and transportation.
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