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Image De-Quantizing via Enforcing Sparseness in Overcomplete Representations

  • Conference paper
Advanced Concepts for Intelligent Vision Systems (ACIVS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3708))

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Abstract

We describe a method for removing quantization artifacts (de-quantizing) in the image domain, by enforcing a high degree of sparseness in its representation with an overcomplete oriented pyramid. For this purpose we devise a linear operator that returns the minimum L2-norm image preserving a set of significant coefficients, and estimate the original by minimizing the cardinality of that subset, always ensuring that the result is compatible with the quantized observation. We implement this solution by alternated projections onto convex sets, and test it through simulations with a set of standard images. Results are highly satisfactory in terms of performance, robustness and efficiency.

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Authors and Affiliations

  1. Visual Information Processing Group, Department of Computer Science and Artificial Inteligence, Universidad de Granada,  

    Luis Mancera & Javier Portilla

Authors
  1. Luis Mancera
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  2. Javier Portilla
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Editor information

Editors and Affiliations

  1. DGA/D4S/MRIS, 94114, Arcueil, France

    Jacques Blanc-Talon

  2. Ghent University, 9000, Gent, Belgium

    Wilfried Philips

  3. Wireless Technologies Lab, CSIRO ICT Centre, NSW 2122, Marsfield, Australia

    Dan Popescu

  4. University of Antwerp, 2610, Wilrijk, Belgium

    Paul Scheunders

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© 2005 Springer-Verlag Berlin Heidelberg

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Mancera, L., Portilla, J. (2005). Image De-Quantizing via Enforcing Sparseness in Overcomplete Representations. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_52

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  • DOI: https://doi.org/10.1007/11558484_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29032-2

  • Online ISBN: 978-3-540-32046-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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