NDLI: Image modeling and enhancement via structured sparse model selection

Content Provider	IEEE Xplore Digital Library
Author	Guoshen Yu Sapiro, G. Mallat, S.
Copyright Year	2010
Description	Author affiliation: ECE, University of Minnesota, USA (Guoshen Yu; Sapiro, G.) \|\| CMAP, Ecole Polytechnique, France (Mallat, S.)
Abstract	An image representation framework based on structured sparse model selection is introduced in this work. The corresponding modeling dictionary is comprised of a family of learned orthogonal bases. For an image patch, a model is first selected from this dictionary through linear approximation in a best basis, and the signal estimation is then calculated with the selected model. The model selection leads to a guaranteed near optimal denoising estimator. The degree of freedom in the model selection is equal to the number of the bases, typically about 10 for natural images, and is significantly lower than with traditional overcomplete dictionary approaches, stabilizing the representation. For an image patch of size √N × √N, the computational complexity of the proposed framework is O $(N^{2}),$ typically 2 to 3 orders of magnitude faster than estimation in an overcomplete dictionary. The orthogonal bases are adapted to the image of interest and are computed with a simple and fast procedure. State-of-the-art results are shown in image denoising, deblurring, and inpainting.
Starting Page	1641
Ending Page	1644
File Size	529919
Page Count	4
File Format	PDF
ISBN	9781424479924
ISSN	15224880
e-ISBN	9781424479948
e-ISBN	9781424479931
DOI	10.1109/ICIP.2010.5653853
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-26
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Image resolution Signal resolution Estimation Linear approximation Noise reduction Computational modeling inpainting Model selection structured sparsity best basis denoising deblurring
Content Type	Text
Resource Type	Article

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