Super-Resolution Imaging
268,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in über 4 Wochen
payback
134 °P sammeln
  • Gebundenes Buch

With contributions from the very top researchers focusing of their areas of expertise, 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 solution, it surveys the latest state of the art techniques in super-resolution imaging. Each detailed chapter provides coverage of the implementations and applications of super-resolution imaging. Its 14 sections span a wide range of modern super-resolution imaging techniques and includes variational, Bayesian, feature-based, multi-channel,…mehr

Andere Kunden interessierten sich auch für
Produktbeschreibung
With contributions from the very top researchers focusing of their areas of expertise, 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 solution, it surveys the latest state of the art techniques in super-resolution imaging. Each detailed chapter provides coverage of the implementations and applications of super-resolution imaging. Its 14 sections span a wide range of modern super-resolution imaging techniques and includes variational, Bayesian, feature-based, multi-channel, learning-based, locally adaptive, and nonparametric methods. It discusses, among others, medical, military, and microscopy applications.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.