Sparsity constrained estimation in image processing and computer vision

Vishal Monga, Hojjat Seyed Mousavi, Umamahesh Srinivas

Research output: Chapter in Book/Report/Conference proceedingChapter


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.

Original languageEnglish (US)
Title of host publicationHandbook of Convex Optimization Methods in Imaging Science
PublisherSpringer International Publishing
Number of pages30
ISBN (Electronic)9783319616094
ISBN (Print)9783319616087
StatePublished - Jan 1 2017

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Engineering


Dive into the research topics of 'Sparsity constrained estimation in image processing and computer vision'. Together they form a unique fingerprint.

Cite this