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Example-Based Super Resolution provides a thorough introduction and overview of example-based super resolution, covering the most successful algorithmic approaches and theories behind them with implementation insights. It also describes current challenges and explores future trends.
Readers of this book will be able to understand the latest natural image patch statistical models and the performance limits of example-based super resolution algorithms, select the best state-of-the-art algorithmic alternative and tune it for specific use cases, and quickly put into practice implementations of the latest and most successful example-based super-resolution methods.
- Provides detailed coverage of techniques and implementation details that have been successfully introduced in diverse and demanding real-world applications
- Covers a wide variety of machine learning approaches, ranging from cross-scale self-similarity concepts and sparse coding, to the latest advances in deep learning
- Presents a statistical interpretation of the subspace of natural image patches that transcends super resolution and makes it a valuable source for any researcher on image processing or low-level vision
Computer vision scientists and researchers with undergraduate-level statistics knowledge whose work is related to imaging. Machine learning, image processing, and research and development communities
- List of Figures
- The Super-Resolution Problem
- Super-Resolution Approaches
- Chapter 1: Classic Multiframe Super Resolution
- 1.1 Problem Statement
- 1.2 Bayesian Inference
- 1.3 Interpolation-Based Methods
- 1.4 Performance Limits
- 1.5 Discussion
- Chapter 2: A Taxonomy of Example-Based Super Resolution
- 2.1 Example-Based Super Resolution
- 2.2 Internal Learning
- 2.3 External Learning
- 2.4 Discussion
- Chapter 3: High-Frequency Transfer
- 3.1 Adaptive Filter Selection
- 3.2 Robustness to Aliasing
- 3.3 Robustness to Noise
- 3.4 Discussion
- Chapter 4: Neighbor Embedding
- 4.1 Framework
- 4.2 Extensions
- 4.3 Performance
- 4.4 Discussion
- Chapter 5: Sparse Coding
- 5.1 Super Resolution Model
- 5.2 Adaptive Extension
- 5.3 Application
- 5.4 Discussion
- Chapter 6: Anchored Regression
- 6.1 Anchored Regression Framework
- 6.2 Extensions
- 6.3 Performance
- 6.4 Discussion
- Chapter 7: Trees and Forests
- 7.1 Hierarchical Manifold Learning
- 7.2 Naive Bayes Super-Resolution Forest
- 7.3 Performance
- 7.4 Discussion
- Chapter 8: Deep Learning
- 8.1 Neural networks
- 8.2 Networks for super resolution
- 8.3 Performance
- 8.4 Discussion
- Chapter 9: Conclusions
- 9.1 Overview
- 9.2 Perspective
- No. of pages:
- © Academic Press 2017
- 23rd September 2016
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Jordi Salvador holds a senior scientist position at Technicolor R&I in Hannover and is member of Technicolor’s Fellowship Network. His main focus is the research of new algorithms for example-based super resolution and machine learning. Formerly, he received a M.Sc. in Telecommunications (equivalent to Electrical) Engineering in 2006 and a M.Sc. in the European MERIT program in 2008, both from the Universitat Polit`ecnica de Catalunya (UPC) in Barcelona. He obtained the Ph.D. degree in 2011, also from UPC, where he contributed to projects of the Spanish Science and Technology System (VISION,
PROVEC) and also to a European FP6 project (CHIL) as research assistant on multiview reconstruction. His research interests include 3D reconstruction, real-time and parallel algorithms, new computer-human interfaces, image and video restoration, super resolution, inverse problems and machine learning.
Senior Scientist, Technicolor R&I, Hannover, Germany
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