Abstract:
Denoising is one of the oldest problems in imaging. But besides removing noise, we've found completely unexpected, brand new uses for denoising. I will give a whirlwind tour of the structure of denoisers, and why denoising is more important than ever; especially now as a core engine and building block for much more complex tasks in imaging, inverse problems, and machine learning.
Dr. Peyman Milanfar - Bio
Dr. Peyman Milanfar
Peyman is a Distinguished Scientist 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 decade, Peyman's team at Google has developed several core imaging technologies at Google. These include the digital zoom pipeline for the Pixel phones, which includes the multi-frame super-resolution (Super Res Zoom) pipeline, and the several generations of digital upscaling algorithms. Most recently, his team led the development of the Unblur feature launched in Google Photos with Pixel 7/pro.
Peyman received his undergraduate education in electrical engineering and mathematics from the UC Berkeley, and the MS and PhD degrees in electrical engineering from the MIT. He holds more than two dozen patents. He founded MotionDSP, which was acquired by Cubic Inc.
Along with his students, he has won three best paper awards from the IEEE Signal Processing Society for nonlinear kernel regression in imaging; the RAISR upscaling algorithm; and neural image quality assessment. He's been 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"