Computational imaging involves the joint design of imaging system hardware and software, optimizing across the entire pipeline from acquisition to reconstruction. Computers can replace bulky and expensive optics by solving computational inverse problems. This talk will describe new microscopes that use computational imaging to enable 3D, super-resolution and phase imaging with simple and inexpensive hardware. Our reconstruction algorithms are based on large-scale nonlinear non-convex optimization with sparsity-based regularizers similar to compressed sensing.
One of the greatest advantages of representing data with graphs is access to generic algorithms for analytic tasks, such as clustering. In this talk, Dr. Schramm will describe some popular graph clustering algorithms, and explain why they are well-motivated from a theoretical perspective. The slides from this presentation can be viewed here.
Presented by GraphXD and BIDS at the University of California, Berkeley, on Thursday, September 28, 2017.