Super-Resolution Imaging (eBook, PDF)

Redaktion: Milanfar, Peyman

• Format: PDF

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 490
• Erscheinungstermin: 19. Dezember 2017
• Englisch
• ISBN-13: 9781439819319
• Artikelnr.: 50530440

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.

Inhaltsangabe

Image Super
Resolution: Historical Overview and Future Challenges. Super
Resolution Using Adaptive Wiener Filters. Locally Adaptive Kernel Regression for Space
Time Super
Resolution. Super
Resolution With Probabilistic Motion Estimation. Spatially Adaptive Filtering as Regularization in Inverse Imaging. Registration for Super
Resolution. Towards Super
Resolution in the Presence of Spatially Varying Blur. Toward Robust Reconstruction
Based Super
Resolution. Multi
Frame Super
Resolution from a Bayesian Perspective. Variational Bayesian Super Resolution Reconstruction. Pattern Recognition Techniques for Image Super
Resolution. Super
Resolution Reconstruction of Multi
Channel Images. New Applications of Super
Resolution in Medical Imaging. Practicing Super
Resolution: What Have We Learned?