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. As an example of end-to-end design, we optimize the encoding mask for a given task-based imaging application and demonstrate whole organism bioimaging and neural activity tracking in vivo.
Speaker: Laura Waller, UC Berkeley
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