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Handbook of Convex Optimization Methods in Imaging Science pp 177–206Cite as

Sparsity Constrained Estimation in Image Processing and Computer Vision

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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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Notes

  1. 1.

    The Matlab code for ICR is available online at http://signal.ee.psu.edu/ICR/ICRpage.htm

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  1. The Pennsylvania State University, University Park, PA, 16802, USA

    Vishal Monga & Hojjat Seyed Mousavi

  2. Apple Inc., Cupertino, CA, 95014, USA

    Umamahesh Srinivas

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  1. Vishal Monga
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    Vishal Monga

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Monga, V., Mousavi, H.S., Srinivas, U. (2018). Sparsity Constrained Estimation in Image Processing and Computer Vision. In: Monga, V. (eds) Handbook of Convex Optimization Methods in Imaging Science. Springer, Cham. https://doi.org/10.1007/978-3-319-61609-4_8

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