Compressive and computational imaging offers the potential for radical new sensor designs coupled with a new way to envision the collection of image information. New theories in sparse Image models, sensor architectures, and reconstruction algorithms all play an integrated role in the design of the next generation imaging sensors. This minisymposium will explore some of the latest theoretical developments, application areas that could benefit from a different sensing paradigm, and current results from prototype compressive and computational imaging devices.

A concept for Wide Field-of-View Imaging

Robert Muise (Lockheed Martin)

Computational sensing approaches for enhanced active imaging

Martin Laurenzis (French-German Research Institute of Saint-Louis)

Time-of-Flight Imaging in Scattering Environments

Marco La Manna (University of Wisconsin - Madison)

Spectral Methods for Passive Imaging: Non-asymptotic Performance and Robustness

Justin Romberg (Georgia Tech)

Efficient Signal Reconstruction for Optically Multiplexed Imagers

Yaron Rachlin (MIT Lincoln Laboratory)

Fast Detection of Compressively-Sensed IR Targets Using Stochastically Trained Least Squares and Compressed Quadratic Correlation Filters

Brian Millikan (University of Central Florida)

DiffuserCam: Lensless Single-exposure 3D Imaging

Laura Waller (University of California, Berkeley)

Phase Retrieval: Tradeoffs and New Algorithms

Christopher Metzler (Rice University)

Imaging and Sensing Around the Corner: An Information-theoretic Approach

Amit Ashok (University of Arizona)

Computer graphics meets estimation theory: Computing parameter estimation lower bounds for non-line-of-sight plenoptic imaging systems

Abhinav V. Sambasivan (University of Minnesota)

UNCOVER: Unconstrained Natural-light Coherency Vector-field-imaging by Exploiting Randomness

Aristide Dogariu (University of Central Florida)

Computational memory effect imaging

Michael Gehm (Duke University)

Organizers:

Richard Baraniuk (Rice University)

Robert Muise (Lockheed Martin)

Keywords:

image reconstruction, image representation, inverse problems