NDLI: Efficient and Robust Image Restoration Using Multiple-Feature L2-Relaxed Sparse Analysis Priors

Content Provider	IEEE Xplore Digital Library
Author	Portilla, J. Tristan-Vega, A. Selesnick, I.W.
Copyright Year	1992
Abstract	We propose a novel formulation for relaxed analysis-based sparsity in multiple dictionaries as a general type of prior for images, and apply it for Bayesian estimation in image restoration problems. Our formulation of a ℓ₂ -relaxed ℓ₀ pseudo-norm prior allows for an especially simple maximum a posteriori estimation iterative marginal optimization algorithm, whose convergence we prove. We achieve a significant speedup over the direct (static) solution by using dynamically evolving parameters through the estimation loop. As an added heuristic twist, we fix in advance the number of iterations, and then empirically optimize the involved parameters according to two performance benchmarks. The resulting constrained dynamic method is not just fast and effective, it is also highly robust and flexible. First, it is able to provide an outstanding tradeoff between computational load and performance, in visual and objective, mean square error and structural similarity terms, for a large variety of degradation tests, using the same set of parameter values for all tests. Second, the performance benchmark can be easily adapted to specific types of degradation, image classes, and even performance criteria. Third, it allows for using simultaneously several dictionaries with complementary features. This unique combination makes ours a highly practical deconvolution method.
Sponsorship	IEEE Signal Processing Society
Starting Page	5046
Ending Page	5059
Page Count	14
File Size	3802122
File Format	PDF
ISSN	10577149
Volume Number	24
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization Kernel Image restoration Dictionaries Convergence Estimation Redundancy robust tunable parameters L2-relaxed L0 pseudo norm maximum a posteriori estimation L2-relaxed sparse analysis priors multiple representations fast constrained dynamic algorithm
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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