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 applicationsCovers a wide variety of machine learning approaches, ranging from cross-scale self-similarity concepts and sparse coding, to the latest advances in deep learningPresents 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 INDICE: Chapter 1. Introduction to super resolutionChapter 2. A historic view of super resolutionChapter 3. Multi-frame super resolutionChapter 4. Example-based super resolutionChapter 5. Cross-scale self-similarityChapter 6. High-frequency transferChapter 7. Locally linear embeddingChapter 8. Robust example-based super resolutionChapter 9. External learningChapter 10. Sparse codingChapter 11. Regression trees and forestsChapter 12. Deep learningChapter 13. Conclusions
- ISBN: 978-0-12-809703-8
- Editorial: Academic Press
- Encuadernacion: Rústica
- Páginas: 85
- Fecha Publicación: 01/11/2016
- Nº Volúmenes: 1
- Idioma: Inglés