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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

8. Sparsity Constrained Estimation in Image Processing and Computer Vision

verfasst von : Vishal Monga, Hojjat Seyed Mousavi, Umamahesh Srinivas

Erschienen in: Handbook of Convex Optimization Methods in Imaging Science

Verlag: Springer International Publishing

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Abstract

Over the past decade, sparsity has emerged as a dominant theme in signal processing and big data applications. In this chapter, we formulate and solve new flavors of sparsity-constrained optimization problems built on the family of spike-and-slab priors. First, we develop an efficient Iterative Convex Refinement solution to the hard non-convex problem of Bayesian signal recovery under sparsity-inducing spike-and-slab priors. We also offer a Bayesian perspective on sparse representation-based classification via the introduction of class-specific priors. This formulation represents a consummation of ideas developed for model-based compressive sensing into a general framework for sparse model-based classification.

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Vorheriges Kapitel Sparsity Based Nonlocal Image Restoration: An Alternating Optimization Approach
Nächstes Kapitel Optimization Problems Associated with Manifold-Valued Curves with Applications in Computer Vision
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The Matlab code for ICR is available online at http://​signal.​ee.​psu.​edu/​ICR/​ICRpage.​htm
 
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Metadaten
Titel
Sparsity Constrained Estimation in Image Processing and Computer Vision
verfasst von
Vishal Monga
Hojjat Seyed Mousavi
Umamahesh Srinivas
Copyright-Jahr
2018
Buch
Handbook of Convex Optimization Methods in Imaging Science

Print ISBN: 978-3-319-61608-7

Electronic ISBN: 978-3-319-61609-4

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-61609-4_8