Image de-quantizing via enforcing sparseness in overcomplete representations

Authors:
Luis Mancera;Javier Portilla
Affiliations:
Visual Information Processing Group, Department of Computer Science and Artificial Inteligence, Universidad de Granada;Visual Information Processing Group, Department of Computer Science and Artificial Inteligence, Universidad de Granada
Venue:
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Year:
2005

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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.