Sparse Signal Recovery Using Markov Random Fields

Abstract

Compressive Sensing (CS) combines sampling and compression into a single sub- Nyquist linear measurement process for sparse and compressible signals. In this paper, we extend the theory of CS to include signals that are concisely represented in terms of a graphical model. In particular, we use Markov Random Fields (MRFs) to represent sparse signals whose nonzero coefficients are clustered. Our new model-based recovery algorithm, dubbed Lattice Matching Pursuit (LaMP), stably recovers MRF-modeled signals using many fewer measurements and computations than the current state-of-the-art algorithms.

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Document Details

Document Type
Technical Report
Publication Date
Dec 20, 2009
Accession Number
ADA520187

Entities

People

  • Chinmay Hegde
  • Marco F. Duarte
  • Richard G. Baraniuk
  • Volkan Cevher

Organizations

  • Rice University

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Coefficients
  • Compressed Sensing
  • Compression
  • Computational Complexity
  • Computational Science
  • Computer Programming
  • Dynamic Range
  • Gaussian Noise
  • Iterations
  • Measurement
  • Optimization
  • Probabilistic Models
  • Probability
  • Recovery
  • Signal Processing
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Graph Algorithms and Convex Optimization.
  • Image Processing and Computer Vision.
  • Statistical inference.