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ESE Faculty Hosted Talk: “Deep Learned Optical Multiplexing for Microscopy”

October 2, 2019 at 2:00 PM - 3:00 PM

Abstract: Fourier ptychographic microscopy is a technique that achieves a high space-bandwidth product, i.e. high resolution and high field-of-view. In Fourier ptychographic microscopy, variable illumination patterns are used to collect multiple low-resolution images. These low-resolution images are then computationally combined to create an image with resolution exceeding that of any single image from the microscope. Due to the necessity of acquiring multiple low-resolution images, Fourier ptychographic microscopy has poor temporal resolution. Our aim is to improve temporal resolution in Fourier ptychographic microscopy, achieving single-shot imaging without sacrificing space-bandwidth product. We use physical preprocessing and example-based super-resolution to achieve this goal by trading off generality of the imaging approach.

In example-based super-resolution, the function relating low-resolution images to their high-resolution counterparts is learned from a given dataset. We take the additional step of optimizing the imaging hardware in order to collect more informative low-resolution images. We show that this “physical preprocessing” allows for improved image reconstruction with deep learning in Fourier ptychographic microscopy.

Vidya Ganapati

Assistant Professor of Engineering, Swarthmore College

Vidya Ganapati is an Assistant Professor of Engineering at Swarthmore College. She was previously a Postdoctoral Associate at Verily Life Sciences. She received her Ph.D. and M.S. in Electrical Engineering & Computer Science at the University of California, Berkeley, and her B.S. at the Massachusetts Institute of Technology. She has been a recipient of the CITRIS Athena Early Career Award, the Department of Energy Office of Science Graduate Fellowship, and the UC Berkeley Chancellor’s Fellowship. Her current research interests include using optimization, machine learning, and simulation for optical system design, with applications in bioimaging and photovoltaics