Image denoising has reached impressive heights in performance and quality -- almost as good as it can ever get. This talk is about the many other things one can do with a good denoiser besides using it for its intended purpose. Of particular interest is how to use denoisers in the regularization of any inverse problem. We propose an explicit image-adaptive regularization functional that makes the overall objective functional clear and well-defined. Remarkably, the resulting regularizer is convex. 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 applications, including tone-mapping, deblurring, and super-resolution.
Peyman leads the Computational Imaging team in Google Research. 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 from the university, serving at Google-x as a visiting scientist, 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 a dozen US patents, several of which are commercially licensed. He founded MotionDSP Inc. in 2005. He has been keynote speaker at numerous technical conferences including Picture Coding Symposium, SIAM Conf. on Imaging Sciences, SPIE, and the International Conference on Multimedia. Along with his students, he 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."