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We describe a computational microscope that encodes 3D information into a single 2D sensor measurement, then exploits sparsity or low-rank priors to reconstruct the volume with diffraction-limited resolution across a large volume. Our system uses simple hardware and scalable software for easy reproducibility and adoption. The inverse algorithm is based on large-scale nonlinear optimization combined with unrolled neural networks, in order to leverage the known physical model of the setup, while learning unknown parameters. We demonstrate whole organism bioimaging and neural activity tracking in vivo
Laura Waller
"Computational microscopy with scattering media", Proc. SPIE 11482, Current Developments in Lens Design and Optical Engineering XXI, 1148202 (25 August 2020); https://doi.org/10.1117/12.2580072
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Laura Waller, "Computational microscopy with scattering media," Proc. SPIE 11482, Current Developments in Lens Design and Optical Engineering XXI, 1148202 (25 August 2020); https://doi.org/10.1117/12.2580072