L1-L2 Optimization in Signal and Image Processing
| Autor: | Michael Zibulevsky, Michael Elad |
|---|---|
| Přispěvatelé: | Department of Computer Science [Haifa], University of Haifa [Haifa], European Project: 225913,EC:FP7:ICT,FP7-ICT-2007-C,SMALL(2009) |
| Rok vydání: | 2010 |
| Předmět: |
Deblurring
Mathematical optimization optimisation Iterative method iterative-shrinkage algorithms Image processing convex optimization problems image restoration fast iterative soft-thresholding algorithm Conjugate gradient method numerical methods Electrical and Electronic Engineering image segmentation conjugate gradient methods Mathematics Signal processing redundant representations Applied Mathematics computed tomography sequential subspace optimization Compressed sensing Signal Processing Convex optimization iterative methods Algorithm design Algorithm signal-image processing |
| Zdroj: | IEEE Signal Processing Magazine IEEE Signal Processing Magazine, Institute of Electrical and Electronics Engineers, 2010, 27 (3), pp.76-88. ⟨10.1109/MSP.2010.936023⟩ |
| ISSN: | 1053-5888 |
| DOI: | 10.1109/msp.2010.936023 |
| Popis: | International audience; Sparse, redundant representations offer a powerful emerging model for signals. This model approximates a data source as a linear combination of few atoms from a prespecified and over-complete dictionary. Often such models are fit to data by solving mixed ¿1-¿2 convex optimization problems. Iterative-shrinkage algorithms constitute a new family of highly effective numerical methods for handling these problems, surpassing traditional optimization techniques. In this article, we give a broad view of this group of methods, derive some of them, show accelerations based on the sequential subspace optimization (SESOP), fast iterative soft-thresholding algorithm (FISTA) and the conjugate gradient (CG) method, present a comparative performance, and discuss their potential in various applications, such as compressed sensing, computed tomography, and deblurring. |
| Databáze: | OpenAIRE |
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