PhD Defense: "Computational Imaging Methods for Improving Resolution in Biological Microscopy"

Kevin G. Chan

March 16th (Thursday), 1:00pm
Engineering Science Building (ESB), Room 2001

With the development of digital cameras, optical microscopy has become an indispensable tool for quantitative measurements in biological research. While there have been many recent advances in microscope architecture and optical design, there is still a need for better imaging solutions as scientists continually seek to perform measurements on smaller scales with higher resolution. Recently, computational imaging has gained popularity as a new way of thinking about image processing. Rather than considering image processing as a separate step, independent of image acquisition, in computational imaging, image acquisition systems and image reconstruction algorithms are jointly designed. In computational microscopy, we apply the computational imaging mindset to microscope system design. By considering image acquisition and image processing interdependently, we can design new integrated imaging solutions for biomicroscopy that surpass the limits of traditional microscopes.

In this talk, I will present computational imaging methods to improve temporal resolution, spatial resolution, and out-of-plane velocity resolution in biological microscopy. In addition, I will demonstrate the application of these methods to in-vivo imaging of live zebrafish. These methods will enable further biological studies of small animals that require imaging in 3D (and time) with high spatio-temporal resolution.

About Kevin G. Chan:

Photo of Kevin ChanKevin G. Chan received his M.S. degree in electrical and computer engineering from the University of California, Santa Barbara, in 2013 and his B.S. degree in engineering from Harvey Mudd College, Claremont, CA, in 2011. He is currently a Ph.D. candidate in the UCSB ECE department and is working in the Systems Bioimaging Laboratory under the supervision of Professor Michael Liebling. His research interests include computational imaging, inverse problems, and their applications to fluorescence microscopy.

Hosted by: Professor Michael Liebling