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

Peyman Milanfar Principal Scientist / Director, Google Research Abstract Denoising is one of the oldest problems in imaging. In the last decade, the quality of denoising algorithms has reached phenomenal levels – almost as good as we can ever hope. There are thousands of papers on this topic, and their scope is vast and approaches so diverse that putting them in some order (as I will do) is both useful and challenging. I’ll describe what we can say about this general class of operators, and what makes them so special. I will argue that denoising is still important, 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. Bio Peyman is a Principal Scientist / 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 S