Talk Abstract: 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. In this talk, I will present a clarifying perspective on denoisers, their structure, and desired properties. The talk will illustrate 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.

Speaker Biography: 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 Unblur, and Zoom Enhance features launched in Google Photos and Pixel devices. 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, including adaptive kernel regression in imaging; the RAISR upscaling algorithm; NIMA: neural image quality assessment, and Regularization by Denoising (RED); all of which have also won 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”