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Super Resolution Optical Flow

Simon Baker and Takeo Kanade
Tech. Report, CMU-RI-TR-99-36, Robotics Institute, Carnegie Mellon University, October, 1999
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Existing approaches to super-resolution are not applicable to videos of faces because faces are non-planar, non-rigid, non-lambertian, and are subject to self occlusion. We present super-resolution optical flow as a solution to these problems. Super-resolution optical flow takes as input a conventional video stream, and simultaneously computes both optical flow and a super-resolution version of the entire video. In this paper we describe an initial implementation of super-resolution optical flow, present detailed experimental results, and describe the relationship between super-resolution optical flow and pyramid-based image representations such as the Laplacian pyramid.


author = {Simon Baker and Takeo Kanade},
title = {Super Resolution Optical Flow},
year = {1999},
month = {October},
institution = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-99-36},
keywords = {Super Resolution, Optical Flow, Video Enhancement, Face Recognition},