| Lecturer Profile |
Dr. YUAN Xinis currently a video coding and analysis lead researcher at Bell Labs, Murray Hill, New Jersey, USA. Prior to this, Dr. Yuan had been a Post-Doctoral Associate/Research Scientist with the Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA, from 2012 to 2015. His research interest lies in signal processing, machine learning, compressive sensing, computer vision, computational photography, and image processing. Xin Yuan has published 26 Journal papers and 23 conference papers in the signal processing area since 2011. His work “Image Translation for Single-shot Focal Tomography” (published in Optica, with Patrick Llull, David Brady and Lawrence Carin) has been reported in Science Daily. Other two papers he co-authored have won the best paper award at Computational Optical Sensing and Imaging (COSI) 2013 and 2014. He has won the travel award for Gordon Research Conference on Imaging Science in 2014 and the IEEE travel grant for Intentional Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2016. He has been reviewers for more than 10 journals in signal processing and image processing, including the IEEE Transactions on Signal Processing, IEEE Transactions on Image Processing IEEE Transactions on Aerospace and Electronic Systems, et al. |
| Lecture Abstract |
Compressive sensing is an important field with continuing advances in theory and applications. Compressive sensing has led to diverse applications, including two-dimensional images, videos, hyperspectral images, depth, x-ray, millimeter wave, polarization, microscopy and Terahertz imaging. Beyond imaging, compressive sensing has also been used in radar signal processing and remote sensing. In this talk, we will start from the space-time0spectrum tradeoff, and introduce the underlying principle of compressive sensing cameras will be described. As a pivot role in compressive sensing reconstruction will be introduced too. Both optimization and Bayesian reconstruction algorithms will be described, taking video compressive sensing as an example. |
