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A new technique of deep learning‐based pixel‐super‐resolved quantitative phase microscopy (DL‐SRQPI) is proposed, achieving rapid wide‐field high‐resolution and high‐throughput quantitative phase imaging (QPI) from single‐shot low‐resolution intensity measurement. By training a neural network with sufficiently paired low‐resolution intensity and high‐resolution phase data, the network is empowered with the capability to robustly reconstruct high‐quality phase information from a single frame of an aliased intensity image. As a graphics processing units‐accelerated computational method with minimal data requirement, DL‐SRQPI is well‐suited for live‐cell imaging and accomplishes high‐throughput long‐term dynamic phase reconstruction. The effectiveness and feasibility of DL‐SRQPI have been significantly demonstrated by comparing it with other traditional and learning‐based phase retrieval methods. The proposed method has been successfully implemented into the quantitative phase reconstruction of biological samples under bright‐field microscopes, overcoming pixel aliasing and improving the spatial‐bandwidth product significantly. The generalization ability of DL‐SRQPI is illustrated by phase reconstruction of Henrietta Lacks cells at various defocus distances and illumination patterns, and its high‐throughput anti‐aliased phase imaging performance is further experimentally validated. Given its capability of achieving pixel super‐resolved QPI from single‐shot intensity measurement over conventional bright‐field microscope hardware, the proposed approach is expected to be widely adopted in life science and biomedical workflows. [ABSTRACT FROM AUTHOR]

Label‐free super‐resolution (LFSR) imaging relies on light‐scattering processes in nanoscale objects without a need for fluorescent (FL) staining required in super‐resolved FL microscopy. The objectives of this Roadmap are to present a comprehensive vision of the developments, the state‐of‐the‐art in this field, and to discuss the resolution boundaries and hurdles that need to be overcome to break the classical diffraction limit of the label‐free imaging. The scope of this Roadmap spans from the advanced interference detection techniques, where the diffraction‐limited lateral resolution is combined with unsurpassed axial and temporal resolution, to techniques with true lateral super‐resolution capability that are based on understanding resolution as an information science problem, on using novel structured illumination, near‐field scanning, and nonlinear optics approaches, and on designing superlenses based on nanoplasmonics, metamaterials, transformation optics, and microsphere‐assisted approaches. To this end, this Roadmap brings under the same umbrella researchers from the physics and biomedical optics communities in which such studies have often been developing separately. The ultimate intent of this paper is to create a vision for the current and future developments of LFSR imaging based on its physical mechanisms and to create a great opening for the series of articles in this field. [ABSTRACT FROM AUTHOR]

Over the past decade, the field of holography has gained significant ground due to advances in computational imaging. However, the utilization of computational tools is hampered by the mismatch between experimental setups and the conceptual model. Differentiable holography (∂H$\bm{\partial }\mathbf {H}$), a novel framework for automatically self‐calibrating experimental imperfections in inverse holographic imaging, is presented here. The technique is demonstrated on auto‐focused complex field imaging from a single intensity‐only inline hologram. [ABSTRACT FROM AUTHOR]

Quantitative phase imaging (QPI), a method that precisely recovers the wavefront of an electromagnetic field scattered by a transparent, weakly scattering object, is a rapidly growing field of study. By solving the inverse scattering problem, the structure of the scattering object can be reconstructed from QPI data. In the past decade, 3D optical tomographic reconstruction methods based on QPI techniques to solve inverse scattering problems have made significant progress. In this review, we highlight a number of these advances and developments. In particular, we cover in depth Fourier transform light scattering (FTLS), optical diffraction tomography (ODT), and white-light diffraction tomography (WDT). [ABSTRACT FROM AUTHOR]