Abstract: 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.
BIDS Data Science Research Seminars feature Berkeley faculty and BIDS collaborators doing visionary research that illustrates the character of data science in this new decade. The series is offered to engage our diverse campus community and to enrich connections, discourse, and discovery among colleagues. All seminars are open to the public, and campus community members are especially encouraged to attend. Arrive half-an-hour early for light refreshments and discussion prior to the formal presentation.
Laura Waller works on computational imaging and microscopy methods for biological, industrial, and commercial applications. She is an associate professor at UC Berkeley in the Department of Electrical Engineering and Computer Sciences (EECS), with affiliations in Bioengineering, QB3, and Applied Sciences & Technology. She was a postdoctoral researcher and lecturer of physics at Princeton University from 2010 to 2012 and received BS, MEng, and PhD degrees from the Massachusetts Institute of Technology in 2004, 2005, and 2010, respectively. She is a Moore Foundation Data-Driven Investigator, Bakar fellow, NSF CAREER awardee, and Packard fellow.