3D intensity and phase imaging from light field measurements in an LED array microscope

By Lei Tian, Laura Waller. 2015

434 citations recorded by the source corpus.

Realizing high resolution across large volumes is challenging for 3D imaging techniques with high-speed acquisition. Here, we describe a new method for 3D intensity and phase recovery from 4D light field measurements, achieving enhanced resolution via Fourier ptychography. Starting from geometric optics light field refocusing, we incorporate phase retrieval and correct diffraction artifacts. Further, we incorporate dark-field images to achieve lateral resolution beyond the diffraction limit of the objective (5× larger NA) and axial resolution better than the depth of field, using a low-magnification objective with a large field of view. Our iterative reconstruction algorithm uses a multislice coherent model to estimate the 3D complex transmittance function of the sample at multiple depths, without any weak or single-scattering approximations. Data are captured by an LED array microscope with computational illumination, which enables rapid scanning of angles for fast acquisition. We demonstrate the method with thick biological samples in a modified commercial microscope, indicating the technique’s versatility for a wide range of applications.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition, Radiation

View Paper: https://www.semanticscholar.org/paper/f3884a84a5103980696e45049f11b780bfe0dabb

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In Atomic and Molecular Physics, and Optics

In Computer Vision and Pattern Recognition

In Radiation

Citations in the corpus

Experimental robustness of Fourier Ptychography phase retrieval algorithms (2015, methodology): “Finally, we discuss the impact of procedures for algorithmic correction of aberrations and mis-calibration.”
High-speed in vitro intensity diffraction tomography (2019, background): “This degradation is intuitively explained under the “light field” [39, 40]”
Computational illumination for high-speed in vitro Fourier ptychographic microscopy (2015, background): “In contrast, FPM provides robustness to focus errors because its DOF is longer than that which would be provided by a high-magnification objective of equivalent NA [17].”
Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media (2018, background): “Several learning-based approaches have been reported recently [21,30,48,49,51,55].”
PhaseMax: Convex Phase Retrieval via Basis Pursuit (2016, background): “Phase retrieval is a well-studied problem with a long history [7], [8] and enjoys widespread use in applications such as X-ray crystallography [9]–[11], microscopy [12], [13], imaging [14], and many more [15]–[18].”
Phase recovery and holographic image reconstruction using deep learning in neural networks (2017, neutral): “…the phase recovery and image reconstruction processes, additional intensity information is recorded, for example, by scanning the illumination source aperture 15 – 18 , sample-to-sensor distance 19 – 23 (in some cases referred to as out-of-focus imaging 24 ), wavelength of illumination 25,26 ,…”
Learning approach to optical tomography (2015, methodology): “Very recently an iterative optimization method was demonstrated [11] for imaging 3D objects using incoherent illumination and intensity detection.”
Compressive light-field microscopy for 3D neural activity recording (2016, background): “Traditional light-field imaging makes a ray optics assumption that breaks down for microscopy, but can be corrected by wave-optics models [14,15].”