This talk will describe new microscopy methods and computational algorithms that use computational imaging to enable 3D phase measurement in samples that are thick or incur multiple scattering, such as embryos or whole organisms. We use image reconstruction algorithms that are based on large-scale nonlinear non-convex optimization combined with unrolled neural networks to model the multiple scattering effects of light passing through the sample. This enables us to reconstruct 3D refractive index maps from angle-coded illumination, even with samples that incur significant scattering. We further discuss engineering of data capture for computational microscopes by end-to-end learned design.
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