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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