Deep learning based coherence holography reconstruction of 3D objects.

We propose a reconstruction method for coherence holography using deep neural networks. cGAN and U-NET models were developed to reconstruct 3D complex objects from recorded interferograms. Our proposed methods, dubbed deep coherence holography (DCH), predict the non-diffracted fields or the sub-objects included in the 3D object from the captured interferograms, yielding better reconstructed objects than the traditional analytical imaging methods in terms of accuracy, resolution, and time.

Three-dimensional refractive index estimation based on deep-inverse non-interferometric optical diffraction tomography (ODT-Deep).

Optical diffraction tomography (ODT) solves an inverse scattering problem to obtain label-free, 3D refractive index (RI) estimation of biological specimens. This work demonstrates 3D RI retrieval methods suitable for partially-coherent ODT systems supported by intensity-only measurements consisting of axial and angular illumination scanning. This framework allows for access to 3D quantitative RI contrast using a simplified non-interferometric technique.

Digital Holographic Microscopy to Assess Cell Behavior.

Digital holographic microscopy is an imaging technique particularly well suited to the study of living cells in culture, as no labeling is required and computed phase maps produce high contrast, quantitative pixel information. A full experiment involves instrument calibration, cell culture quality checks, selection and setup of imaging chambers, a sampling plan, image acquisition, phase and amplitude map reconstruction, and parameter map post-processing to extract information about cell morphology and/or motility. Each step is described below, focusing on results from imaging four human cell lines.

Dual-modality digital holographic and polarization microscope to quantify phase and birefringence signals in biospecimens with a complex microstructure.

Optical phase and birefringence signals occur in cells and thin, semi-transparent biomaterials. A dual-modality quantitative phase and polarization microscope was designed to study the interaction of cells with extracellular matrix networks and to relate optical pathlength and birefringence signals within structurally anisotropic biomaterial constructs. The design was based on an existing, custom-built digital holographic microscope, to which was added a polarization microscope utilizing liquid crystal variable retarders.

Computational multi-wavelength phase synthesis using convolutional neural networks [Invited].

Multi-wavelength digital holographic microscopy (MWDHM) provides indirect measurements of the refractive index for non-dispersive samples. Successive-shot MWDHM is not appropriate for dynamic samples and single-shot MWDHM significantly increases the complexity of the optical setup due to the need for multiple lasers or a wavelength tunable source. Here we consider deep learning convolutional neural networks for computational phase synthesis to obtain high-speed simultaneous phase estimates on different wavelengths and thus single-shot estimates of the integral refractive index without increased experimental complexity.