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ACIVS
2005
Springer

Image De-Quantizing via Enforcing Sparseness in Overcomplete Representations

9 years 9 months ago
Image De-Quantizing via Enforcing Sparseness in Overcomplete Representations
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.
Luis Mancera, Javier Portilla
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where ACIVS
Authors Luis Mancera, Javier Portilla
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