Abstract Emerging technologies have given us an unprecedented ability to measure and manipulate light: We can now time-stamp individual photons and adaptively shape the phase profile of a laser beam. These capabilities stand to fundamentally change how we approach many imaging problems. However, using these capabilities effectively requires us to rethink how we process optical signals. Statistical signal processing is a powerful lens through which to view imaging. It allows us to abstract complex physical problems into manageable representations and develop unconventional solutions. In this talk I will briefly discuss how statistical signal processing can be used to solve four extreme imaging problems - problems for which conventional imaging techniques are doomed to fail: (1) Reconstructing a hidden object from measurements captured through the keyhole of a door. (2) Imaging through 27 attenuation lengths of fog. (3) Characterizing scattering media with intensity- only measurements. (4) Single-pixel compressive imaging without explicit priors nor ground-truth training data.
Bio: Chris Metzler is an Intelligence Community Postdoctoral Research Fellow in the Stanford Computational Imaging Lab. Prior to that, he was an NSF and NDSEG Graduate Research Fellow in the Digital Signal Processing Lab at Rice University. His research applies signal processing and machine learning to solve challenging computational imaging problems.