Regularization by Denoising (RED)

Thursday April 13, 2017 at 10:00 a.m. Dr. Peyman Milanfar, Leader of Computational Imaging team in Google Research, will be presenting an IEEE Signal Processing Society Distinguished Lecture, “Regularization by Denoising (RED)”.

Day & Time: Thursday April 13, 2017
10:00 a.m. – 11:00 p.m.

Speaker: Dr. Peyman Milanfar
Leader of Computational Imaging team in Google Research
Visiting Faculty at Electrical Engineering Department, UC Santa Cruz

Location: University of Toronto, Bahen Center (Room BA 5281)
40 St. George Street, Toronto, ON M5S 2E4
https://goo.gl/maps/7ick2cparLF2

Contact: Mehrnaz Shokrollahi

Organizers: IEEE Signal Processing Chapter Toronto Section

Abstract: Image denoising is the most fundamental problem in image enhancement, and it is largely solved: It has reached impressive heights in performance and quality — almost as good as it can ever get. But interestingly, it turns out that we can solve many other problems using the image denoising “engine”. I will describe the Regularization by Denoising (RED) framework: using the denoising engine in defining the regularization of any inverse problem. The idea is to define an explicit image-adaptive regularization functional directly using a high performance denoiser. Surprisingly, the resulting regularizer is guaranteed to be convex, and the overall objective functional is explicit, clear and well-defined. With complete flexibility to choose the iterative optimization procedure for minimizing this functional, RED is capable of incorporating any image denoising algorithm as a regularizer, treat general inverse problems very effectively, and is guaranteed to converge to the globally optimal result.

Biography: Peyman leads the Computational Imaging/ Image Processing team in Google Research. Prior to this, he was a Professor of Electrical Engineering at UC Santa Cruz from 1999-2014, where he is now a visiting faculty. 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. 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 11 US patents, several of which are commercially licensed. He founded MotionDSP in 2005. He 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 Fellow of the IEEE “for contributions to inverse problems and super-resolution in imaging.”