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Sparse Signal Recovery Using Markov Random Fields
Volkan Cevher · Marco F Duarte · Chinmay Hegde · Richard Baraniuk

Wed Dec 10 05:23 PM -- 05:24 PM (PST) @
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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 reconstruction 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.

Author Information

Volkan Cevher (EPFL)
Marco F Duarte (University of Massachusetts)
Chinmay Hegde (Rice University)
Richard Baraniuk (Rice University)

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