Between ever increasing pixel counts, ever cheaper sensors, and the ever expanding world-wide-web, natural image data has become plentiful. These vast quantities of data, be they high frame rate videos or huge curated datasets like Imagenet, stand to substantially improve the performance and capabilities of computational imaging systems. However, using this data efficiently presents its own unique set of challenges. In this talk I will use data to develop better priors, improve reconstructions, and enable new capabilities for computational imaging systems.
Chris Metzler is a PhD candidate in the Machine Learning, Digital Signal Processing, and Computational Imaging labs at Rice University. His research focuses on developing and applying new algorithms, including neural networks, to problems in computational imaging. Much of his work concerns imaging through scattering media, like fog and water, and last summer he interned in the U.S. Naval Research Laboratory's Applied Optics branch. He is an NSF graduate research fellow and was formerly an NDSEG graduate research fellow.