Peyman Milanfar plenary: Data-adaptive Filtering and the State of the Art in Image Processing

A plenary talk from SPIE Optics + Photonics 2014.

10 October 2014

The most effective recent approaches to processing and restoration of images and video are ones that flexibly adapt themselves to the content of these signals. These high performance methods have come about through the convergence of several powerful ideas from different science and engineering disciplines.

Examples include Moving Least Square (from computer graphics), the Bilateral Filter and Anisotropic Diffusion (from computer vision), Boosting and Spectral Methods (from Machine Learning), Non-local Means and Bregman Iterations (from Applied Math), Kernel Regression and Iterative Scaling (from Statistics). In this plenary talk, Peyman Milanfar of the University of California, Santa Cruz (USA) presents a framework for understanding many common underpinnings of these ideas.

Peyman Milanfar received his undergraduate education in electrical engineering and mathematics from the University of California, Berkeley, and MS and PhD degrees in electrical engineering from the Massachusetts Institute of Technology. He has been on the EE faculty at UC Santa Cruz since 1999, having served as Associate Dean of the School of Engineering from 2010-12. Since 2012 he has been on leave at Google-X, where he was recruited to work on computational photography and more specifically, on the imaging pipeline for Google Glass.

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