Super-Resolution Imaging
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This book functions as the definitive overview of the field of super-resolution imaging. Written by the leading researchers in the field of image and video super-resolution, it surveys state-of-the-art techniques, and each detailed chapter covers the implementations and applications of super-resolution imaging. Its 14 sections span a wide range of modern super-resolution imaging methods and includes variational, Bayesian, feature-based, multi-channel, learning-based, locally adaptive, and nonparametric methods. It discusses, among others, medical, military, and remote-sensing applications. The…mehr

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Produktbeschreibung
This book functions as the definitive overview of the field of super-resolution imaging. Written by the leading researchers in the field of image and video super-resolution, it surveys state-of-the-art techniques, and each detailed chapter covers the implementations and applications of super-resolution imaging. Its 14 sections span a wide range of modern super-resolution imaging methods and includes variational, Bayesian, feature-based, multi-channel, learning-based, locally adaptive, and nonparametric methods. It discusses, among others, medical, military, and remote-sensing applications. The book can be used as a reference, a basis for short courses on the subject, or as part of a graduate course on digital image processing.
Autorenporträt
Peyman Milanfar is Professor of Electrical Engineering at the University of California, Santa Cruz. He received a B.S. degree in Electrical Engineering/Mathematics from the University of California, Berkeley, and the Ph.D. degree in Electrical Engineering from the Massachusetts Institute of Technology. Prior to coming to UCSC, he was at SRI (formerly Stanford Research Institute) and served as a Consulting Professor of computer science at Stanford. In 2005 he founded MotionDSP, Inc., to bring state-of-art video enhancement technology to consumer and forensic markets. He is a Fellow of the IEEE for contributions to Inverse Problems and Super-resolution in Imaging.