Brief Biography

Updated March 2015

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 was a Professor of EE 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. He currently leads the Computational Imaging team in Google Research. He holds 8 US patents, several of which are commercially licensed. He founded MotionDSP in 2005. He has been keynote speaker at numerous technical conferences including PCS, SPIE, and ICME; and 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."

Recent News

Updated March 2015

Paper Highlights

Updated March 2015

A. Kheradmand and P. Milanfar, "A General Framework for Regularized, Similarity-based Image Restoration", IEEE Transactions on Image Processing, vol. 23, no. 12, pp. 5136-5151, Dec. 2014
We've developed an iterative graph-based framework for image restoration based on a new definition of the normalized graph Laplacian. We propose a cost function which consists of a new data fidelity term and a regularization term derived from the specific definition of the normalized graph Laplacian. The specific form of the cost function allows us to render the spectral analysis for the algorithm. The approach is general in the sense that we have shown its effectiveness for different restoration problems including deblurring, denoising, and sharpening. Teaser Image
H. Talebi and P. Milanfar, "Nonlocal Image Editing", IEEE Transactions on Image Processing, vol. 23, no. 10, pp. 4460-4473, Oct. 2014
This is a new image editing tool, based on the spectrum of a global filter computed from image affinities. The orthonormal eigenvectors of the filter matrix are highly expressive of the coarse and fine details in the underlying image. Each eigenvalue can boost or suppress the corresponding signal component in each scale. This endows the filter with a number of important capabilities, such as edge-aware sharpening, tone manipulation and abstraction, to name a few. The edits can be easily propagated across the image Teaser Image