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      Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery

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          Abstract

          Holography encodes the three dimensional (3D) information of a sample in the form of an intensity-only recording. However, to decode the original sample image from its hologram(s), auto-focusing and phase-recovery are needed, which are in general cumbersome and time-consuming to digitally perform. Here we demonstrate a convolutional neural network (CNN) based approach that simultaneously performs auto-focusing and phase-recovery to significantly extend the depth-of-field (DOF) in holographic image reconstruction. For this, a CNN is trained by using pairs of randomly de-focused back-propagated holograms and their corresponding in-focus phase-recovered images. After this training phase, the CNN takes a single back-propagated hologram of a 3D sample as input to rapidly achieve phase-recovery and reconstruct an in focus image of the sample over a significantly extended DOF. This deep learning based DOF extension method is non-iterative, and significantly improves the algorithm time-complexity of holographic image reconstruction from O(nm) to O(1), where n refers to the number of individual object points or particles within the sample volume, and m represents the focusing search space within which each object point or particle needs to be individually focused. These results highlight some of the unique opportunities created by data-enabled statistical image reconstruction methods powered by machine learning, and we believe that the presented approach can be broadly applicable to computationally extend the DOF of other imaging modalities.

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          Imaging without lenses: achievements and remaining challenges of wide-field on-chip microscopy.

          We discuss unique features of lens-free computational imaging tools and report some of their emerging results for wide-field on-chip microscopy, such as the achievement of a numerical aperture (NA) of ∼0.8-0.9 across a field of view (FOV) of more than 20 mm(2) or an NA of ∼0.1 across a FOV of ∼18 cm(2), which corresponds to an image with more than 1.5 gigapixels. We also discuss the current challenges that these computational on-chip microscopes face, shedding light on their future directions and applications.
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            Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications.

            Despite the rapid progress in optical imaging, most of the advanced microscopy modalities still require complex and costly set-ups that unfortunately limit their use beyond well equipped laboratories. In the meantime, microscopy in resource-limited settings has requirements significantly different from those encountered in advanced laboratories, and such imaging devices should be cost-effective, compact, light-weight and appropriately accurate and simple to be usable by minimally trained personnel. Furthermore, these portable microscopes should ideally be digitally integrated as part of a telemedicine network that connects various mobile health-care providers to a central laboratory or hospital. Toward this end, here we demonstrate a lensless on-chip microscope weighing approximately 46 grams with dimensions smaller than 4.2 cm x 4.2 cm x 5.8 cm that achieves sub-cellular resolution over a large field of view of approximately 24 mm(2). This compact and light-weight microscope is based on digital in-line holography and does not need any lenses, bulky optical/mechanical components or coherent sources such as lasers. Instead, it utilizes a simple light-emitting-diode (LED) and a compact opto-electronic sensor-array to record lensless holograms of the objects, which then permits rapid digital reconstruction of regular transmission or differential interference contrast (DIC) images of the objects. Because this lensless incoherent holographic microscope has orders-of-magnitude improved light collection efficiency and is very robust to mechanical misalignments it may offer a cost-effective tool especially for telemedicine applications involving various global health problems in resource limited settings.
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              Spatial filtering for zero-order and twin-image elimination in digital off-axis holography

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                Author and article information

                Journal
                21 March 2018
                Article
                1803.08138
                bcb519d4-c0fd-44a3-8b38-652726a4ba2f

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                68T01, 68T05, 68U10, 62M45, 78M32, 92C55, 94A08
                cs.CV cs.LG physics.optics

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