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.

• Type: Conference
• Year: 2005
• Where: ACIVS
• Authors: Luis Mancera, Javier Portilla
• Keywords: ACIVS 2005, Computer Vision, Minimum L2-norm Image, Overcomplete Oriented Pyramid, Quantization Artifacts