Abstract: Image denoising has reached impressive heights in performance and quality -- almost as good as it can ever get. But this isn't the only way in which tasks in image processing can exploit the image denoising engine. I will describe Regularization by Denoising (RED): using the denoising engine in defining the regularization of any inverse problem. We propose an explicit image-adaptive Laplacian-based regularization functional that makes the overall objective functional 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. I will show examples of its utility, including state-of-the-art results in image deblurring and super-resolution problems.
Bio: 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, 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." His favorite temperature is 72 degrees Fahrenheit.