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· 분류 : 외국도서 > 과학/수학/생태 > 과학 > 물리학 > 광학
· ISBN : 9781439819302
· 쪽수 : 490쪽
· 출판일 : 2010-09-07
목차
Image Super-Resolution: Historical Overview and Future Challenges, J. Yang and T. Huang
Introduction to Super-Resolution
Notations
Techniques for Super-Resolution
Challenge issues for Super-Resolution
Super-Resolution Using Adaptive Wiener Filters, R.C. Hardie
Introduction
Observation Model
AWF SR Algorithms
Experimental Results
Conclusions
Acknowledgments
Locally Adaptive Kernel Regression for Space-Time Super-Resolution, H. Takeda and P. Milanfar
Introduction
Adaptive Kernel Regression
Examples
Conclusion
AppendiX
Super-Resolution With Probabilistic Motion Estimation, M. Protter and M. Elad
Introduction
Classic Super-Resolution: Background
The Proposed Algorithm
Experimental Validation
Summary
Spatially Adaptive Filtering as Regularization in Inverse Imaging, A. Danielyan, A. Foi, V. Katkovnik, and K. Egiazarian
Introduction
Iterative filtering as regularization
Compressed sensing
Super-resolution
Conclusions
Registration for Super-Resolution, P. Vandewalle, L. Sbaiz, and M. Vetterli
Camera Model
What Is Resolution?
Super-Resolution as a Multichannel Sampling Problem
Registration of Totally Aliased Signals
Registration of Partially Aliased Signals
Conclusions
Towards Super-Resolution in the Presence of Spatially Varying Blur, M. Sorel, F. Sroubek and J. Flusser
Introduction
Defocus and Optical Aberrations
Camera Motion Blur
Scene Motion
Algorithms
Conclusion
Acknowledgments
Toward Robust Reconstruction-Based Super-Resolution, M. Tanaka and M. Okutomi
Introduction
Overviews
Robust SR Reconstruction with Pixel Selection
Robust Super-Resolution Using MPEG Motion Vectors
Robust Registration for Super-Resolution
Conclusions
Multi-Frame Super-Resolution from a Bayesian Perspective, L. Pickup, S. Roberts, A. Zisserman and D. Capel
The Generative Model
Where Super-Resolution Algorithms Go Wrong
Simultaneous Super-Resolution
Bayesian Marginalization
Concluding Remarks
Variational Bayesian Super Resolution Reconstruction, S. Derin Babacan, R. Molina, and A.K. Katsaggelos
Introduction
Problem Formulation
Bayesian Framework for Super Resolution
Bayesian Inference
Variational Bayesian Inference Using TV Image Priors
Experiments
Estimation of Motion and Blur
Conclusions
Acknowledgements
Pattern Recognition Techniques for Image Super-Resolution, K. Ni and T.Q. Nguyen
Introduction
Nearest Neighbor Super-Resolution
Markov Random Fields and Approximations
Kernel Machines for Image Super-Resolution
Multiple Learners and Multiple Regressions
Design Considerations and Examples
Remarks
Glossary
Super-Resolution Reconstruction of Multi-Channel Images, O.G. Sezer and Y. Altunbasak
Introduction
Notation
Image Acquisition Model
Subspace Representation
Reconstruction Algorithm
Experiments & Discussions
Conclusion
New Applications of Super-Resolution in Medical Imaging, M.D.Robinson, S.J. Chiu, C.A. Toth, J.A. Izatt, J.Y. Lo, and S. Farsiu
Introduction
The Super-Resolution Framework
New Medical Imaging Applications
Conclusion
Acknowledgment
Practicing Super-Resolution: What Have We Learned? N. Bozinovic
Abstract
Introduction
MotionDSP: History and Concepts
Markets and Applications
Technology
Results
Lessons Learned
Conclusions
