AM Seminar: Denoising: A Powerful Building Block for Imaging, Inverse Problems and Machine Learning
Presenter: Peyman Milanfar, Distinguished Scientist, Google
Description: Denoising, the process of reducing random fluctuations in a signal to emphasize essential patterns, has been a fundamental problem of interest since the dawn of modern scientific inquiry. Recent denoising techniques, particularly in imaging, have achieved remarkable success, nearing theoretical limits by some measures. Yet, despite tens of thousands of research papers, the wide-ranging applications of denoising beyond noise removal have not been fully recognized. This is partly due to the vast and diverse literature, making a clear overview challenging. This article aims to address this gap. We present a clarifying perspective on denoisers, their structure and their desired properties. We emphasize the increasing importance of denoising and showcase its evolution into an essential building block for complex tasks in imaging, inverse problems and machine learning. Despite its long history, the community continues to uncover unexpected and groundbreaking uses for denoising, further solidifying its place as a cornerstone of scientific and engineering practice.
Bio: Peyman is a Distinguished Scientist at Google, where he leads the Computational Imaging team. Prior to this, he was a Professor of Electrical Engineering at UC Santa Cruz for 15 years, two of those as Associate Dean for Research. 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 that are used in many products. Among these are the zoom pipeline for the Pixel phones, which includes the multi-frame super-resolution (Super Res Zoom) pipeline, and several generations of state of the art digital upscaling algorithms. Most recently, his team led the development of Pro Res Zoom, and Unblur, features launched in Pixel 10 devices and Google Photos. Peyman received his undergraduate education in electrical engineering and mathematics from the UC Berkeley, and the MS and PhD degrees in electrical engineering from MIT. He holds more than two dozen patents. He founded MotionDSP, which was acquired by Cubic Inc. Along with his students and colleagues, his research work has had deep impact in several areas of computational imaging, and applications of AI thereto – including the introduction of adaptive kernel regression to imaging; pioneering use of learning for fast, content-adaptive image upscaling (RAISR); Neural Image quality Assessment (NIMA), Regularization by Denoising (RED); and most recently (2024) Inversion by Direct Iteration (InDI). All of these works have been recognized with best paper awards. 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”
Hosted by: Professor Julie Simons, Applied Mathematics
Zoom link: https://ucsc.zoom.us/j/
Meeting ID: 936 8117 5800
Passcode: 609643
