Sparse and Redundant Representations From Theory to Applications in Signal and Image Processing /

The field of sparse and redundant representation modeling has gone through a major revolution in the past two decades. This started with a series of algorithms for approximating the sparsest solutions of linear systems of equations, later to be followed by surprising theoretical results that guarant...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Elad, Michael (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2010.
Edición:1st ed. 2010.
Temas:
Mathematical analysis.
Mathematical models.
Mathematical optimization.
Approximation theory.
Computer vision.
Signal processing.
Analysis.
Mathematical Modeling and Industrial Mathematics.
Optimization.
Approximations and Expansions.
Computer Vision.
Signal, Speech and Image Processing .
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Sparse and Redundant Representations - Theoretical and Numerical Foundations
  • Prologue
  • Uniqueness and Uncertainty
  • Pursuit Algorithms - Practice
  • Pursuit Algorithms - Guarantees
  • From Exact to Approximate Solutions
  • Iterative-Shrinkage Algorithms
  • Towards Average PerformanceAnalysis
  • The Dantzig-Selector Algorithm
  • From Theory to Practice - Signal and Image Processing Applications
  • Sparsity-Seeking Methods in Signal Processing
  • Image Deblurring - A Case Study
  • MAP versus MMSE Estimation
  • The Quest for a Dictionary
  • Image Compression - Facial Images
  • Image Denoising
  • Other Applications
  • Epilogue.

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