Milanfar Super-Resolution Imaging

Erscheinungsjahr 2010
ISBN: 978-1-4398-1931-9
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 490 Seiten

Reihe: Digital Imaging and Computer Vision

With the exponential increase in computing power and broad proliferation of digital cameras, super-resolution imaging is poised to become the next "killer app." The growing interest in this technology has manifested itself in an explosion of literature on the subject. Super-Resolution Imaging consolidates key recent research contributions from eminent scholars and practitioners in this area and serves as a starting point for exploration into the state of the art in the field. It describes the latest in both theoretical and practical aspects of direct relevance to academia and industry, providing a base of understanding for future progress.
Features downloadable tools to supplement material found in the book

Recent advances in camera sensor technology have led to an increasingly larger number of pixels being crammed into ever-smaller spaces. This has resulted in an overall decline in the visual quality of recorded content, necessitating improvement of images through the use of post-processing. Providing a snapshot of the cutting edge in super-resolution imaging, this book focuses on methods and techniques to improve images and video beyond the capabilities of the sensors that acquired them. It covers:

- History and future directions of super-resolution imaging

- Locally adaptive processing methods versus globally optimal methods

- Modern techniques for motion estimation

- How to integrate robustness

- Bayesian statistical approaches

- Learning-based methods

- Applications in remote sensing and medicine

- Practical implementations and commercial products based on super-resolution

The book concludes by concentrating on multidisciplinary applications of super-resolution for a variety of fields. It covers a wide range of super-resolution imaging implementation techniques, including variational, feature-based, multi-channel, learning-based, locally adaptive, and nonparametric methods. This versatile book can be used as the basis for short courses for engineers and scientists, or as part of graduate-level courses in image processing.

Zielgruppe

Researchers and technicians in image and video processing and computer vision, computer science, and applied mathematics; including electrical and industrial engineers

Autoren/Hrsg.

Milanfar, Peyman

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning

Inhaltsverzeichnis

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

Über Autor(innen)

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.