Title: Denoising as a Building Block: Form, function, and regularization of inverse problems
Abstract: Denoising of images has reached impressive levels of quality — almost as good as we can ever hope. There are thousands of papers on this topic, and their scope is so vast and approaches so diverse that putting them in some order is useful and challenging. I will speak about why we should still care deeply about this topic, what we can say about this general class of operators on images, and what makes them so special. Of particular interest is how we can use denoisers as building blocks for broader image processing tasks, including as regularizers for general inverse problems.
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 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. Most recently, Peyman’s team at Google developed the digital zoom pipeline for the Pixel phones, which includes the multi-frame super-resolution (“Super Res Zoom”) pipeline, and the RAISR upscaling algorithm. In addition, the Night Sight mode on Pixel 3 uses our Super Res Zoom technology to merge images (whether you zoom or not) for vivid shots in low light.
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 15 patents, several of which are commercially licensed. He founded MotionDSP, which was acquired by Cubic Inc. (NYSE:CUB).
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 is a Distinguished Lecturer of the IEEE Signal Processing Society, and a Fellow of the IEEE “for contributions to inverse problems and super-resolution in imaging.”
Here is the new link and meeting ID+passcode:
Meeting ID: 914 6737 5713