Example-Based Super Resolution

Nonfiction, Computers, Advanced Computing, Engineering, Optical Data Processing, Application Software, General Computing
Cover of the book Example-Based Super Resolution by Jordi Salvador, Elsevier Science
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Author: Jordi Salvador ISBN: 9780081011355
Publisher: Elsevier Science Publication: September 22, 2016
Imprint: Academic Press Language: English
Author: Jordi Salvador
ISBN: 9780081011355
Publisher: Elsevier Science
Publication: September 22, 2016
Imprint: Academic Press
Language: English

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
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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