Super-Resolution Imaging

Super-Resolution Imaging

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Features

  • Presents a comprehensive overview of the field, surveying the latest techniques in super-resolution imaging
  • Provides detailed coverage of implementations and applications
  • Contains numerous examples, illustrations, tables and figures
  • Features top contributors from the USA, the UK, Israel, Japan, and the European Union

Summary

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.

Table of Contents

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

Editor Bio(s)

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.

 
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