Denoising as a Building Block for Imaging, Inverse Problems, and Machine Learning

Denoising is one of the oldest problems in imaging. There are thousands of papers on this topic, and their scope is vast and the approaches so diverse that putting them in some order (as I will do) is both useful and challenging. In the last decade, the quality of denoising algorithms has reached phenomenal levels – almost as good as we can ever hope. But besides this, we've found completely unexpected, brand new uses for denoising. I will describe what we can say about this general class of operators, and what makes them so special. I will argue that denoising is more important than ever; not simply as a process for removing noise, but especially now as a core engine and building block for much more complex tasks in imaging, inverse problems, and machine learning.

Speaker Biography:
Peyman is a Distinguished Scientist / Senior Director at Google Research, where he leads the Computational Imaging team. Prior to this, he was a Professor of Electrical Engineering at UC Santa Cruz from 1999-2014. He was Associate Dean for Research at the School of Engineering from 2010-12. From 2012-2014 he was on leave at Google-x, where he helped develop the imaging pipeline for Google Glass. Over the last several years, Peyman's team at Google has developed several core technologies including the digital zoom pipeline for the Pixel phones, which includes the multi-frame super-resolution (Super Res Zoom) pipeline (blog, and project website), and the RAISR upscaling algorithm. Most recently, his team led the development of the Unblur feature launched with Pixel 7/pro (announcement) (advertisement)(review) Peyman received his undergraduate education in electrical engineering and mathematics from the University of California, Berkeley, and the MS and PhD degrees in electrical engineering from the Massachusetts Institute of Technology. He holds numerous patents, several of which are commercially licensed. He founded MotionDSP, which was acquired by Cubic Inc. Peyman has been keynote speaker at numerous technical conferences including Picture Coding Symposium (PCS), SIAM Imaging Sciences, SPIE, and the International Conference on Multimedia (ICME). Along with his students, he has won several best paper awards from the IEEE Signal Processing Society. He was a Distinguished Lecturer of the IEEE Signal Processing Society, and is a Fellow of the IEEE "for contributions to inverse problems and super-resolution in imaging."