Example-Based Super Resolution

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

Chapter 1: Classic Multiframe Super Resolution

Chapter 2: A Taxonomy of Example-Based Super Resolution

Chapter 3: High-Frequency Transfer

Chapter 4: Neighbor Embedding

Chapter 5: Sparse Coding

Chapter 6: Anchored Regression

Chapter 7: Trees and Forests

Chapter 8: Deep Learning

Chapter 9: Conclusions