Brief Biography

Updated May 2016

Peyman leads the Computational Imaging/ Image Processing team in Google Research. Prior to this, 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. 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 10 US patents, several of which are commercially licensed. He founded MotionDSP in 2005. He has been keynote speaker at numerous technical conferences including PCS, SIAM Imaging, 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 May 2016

  • May 2016: I gave a plenary talk at the SIAM Conference on Imaging Sciences. The slides and video of my talk are available here . More information about the conference here .
  • July 2015: I gave a plenary talk at the International Conference on Multimedia and Expo in Torino, Italy. More details here, including slides of my talk.
  • April 2015: devCam: Open-source Android Camera Control for Algorithm Development and Testing --
    Rob Sumner has built an open-source app that makes it easy for researchers in computational photography, vision, and image processing to capture images/burts, do experiments, and test their algorithms in this area. d
  • November 2014: My student Sujoy Biswas Kumar won the Best Student Paper Award at ICIP 2014.
  • October 2014: I gave a plenary talk at the SPIE Optics and Photonics Conference in San Diego. Here is the video of my talk.
  • May 2014: I gave a plenary talk at the Technion's TCE Conference . Here is the video of my talk along with the slides .

Paper Highlights

Updated September 2016

Y. Romano, J. Isidoro, and P. Milanfar, "RAISR: Rapid and Accurate Image Super-Resolution ", arXiv:1606.01299 Submitted
With sufficient training data (corresponding pairs of low and high resolution images) we can learn sets of filters (i.e. a mapping) that when applied to given image that is not in the training set, will produce a higher resolution version of it, where the learning is low complexity. In our approach, the run-time is more than one to two orders of magnitude faster than the best competing methods currently available, while producing results comparable or better than state-of-the-art. Teaser Image
M. Elad and P. Milanfar, "Style Transfer via Texture Synthesis ", arXiv:1609.03057 Submitted
We propose a novel style-transfer algorithm that extends the texture-synthesis work of Kwatra et. al. (2005), while aiming to get stylized images that get closer in quality to ones produced by Convolutional Neural Networks. The results obtained are visually pleasing and diverse, shown to be competitive with the recent CNN style-transfer algorithms. The proposed algorithm is fast and flexible, being able to process any pair of content + style images . Teaser Image