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
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.
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?
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?
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