Bishara et al., Holographic pixel super-resolution in portable lensless on-chip microscopy using a fiber-optic array. Ozcan, Lensless digital holographic microscopy and its applications in biomedicine and environmental monitoring. Elser, Phase retrieval by iterated projections. Fienup, Phase retrieval algorithms: a comparison. Fink, Solution to the twin image problem in holography. Gerchberg, A practical algorithm for the determination of phase from image and diffraction plane pictures. Greenbaum et al., Imaging without lenses: achievements and remaining challenges of wide-field on-chip microscopy. Ozcan, Mobile phones democratize and cultivate next-generation imaging, diagnostics and measurement tools. Finally, we discuss new opportunities for exploiting domain adaptation techniques and physics-integrated approaches in lensless imaging.Ī. We also highlight some unique capabilities of the DL approaches, including lensless imaging with an extended depth-of-field (DOF) or virtual staining. These DL approaches include the supervised learning approach with paired training datasets and the unsupervised learning approach with unpaired training datasets or without any ground truth data. Here, we review the recent applications of deep learning (DL) algorithms in holographic image reconstruction that are proposed to achieve robust and fast holographic reconstruction in lensless imaging. However, due to the limited capability of the traditional algorithms, such as excessive processing time and high chance of failure in confluent specimens, lensless imaging has not been practically used in the relevant application areas. This holographic reconstruction has been traditionally implemented by iterative phase retrieval algorithms. In lensless imaging, the objects’ complex amplitude information is computationally reconstructed from the diffracted intensity measured on a sensor plane. Lensless imaging is an imaging modality that allows high-resolution and large field-of-view (FOV) imaging with cost-effective and portable devices.
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