Efficient adaptive compressive sensing using sparse hierarchical learned dictionaries

Akshay Soni, Jarvis D Haupt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

Recent breakthrough results in compressed sensing (CS) have established that many high dimensional objects can be accurately recovered from a relatively small number of non-adaptive linear projection observations, provided that the objects possess a sparse representation in some basis. Subsequent efforts have shown that the performance of CS can be improved by exploiting the structure in the location of the non-zero signal coefficients (structured sparsity) or using some form of online measurement focusing (adaptivity) in the sensing process. In this paper we examine a powerful hybrid of these two techniques. First, we describe a simple adaptive sensing procedure and show that it is a provably effective method for acquiring sparse signals that exhibit structured sparsity characterized by tree-based coefficient dependencies. Next, employing techniques from sparse hierarchical dictionary learning, we show that representations exhibiting the appropriate form of structured sparsity can be learned from collections of training data. The combination of these techniques results in an effective and efficient adaptive compressive acquisition procedure.

Original languageEnglish (US)
Title of host publicationConference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Pages1250-1254
Number of pages5
DOIs
StatePublished - Dec 1 2011
Event45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 - Pacific Grove, CA, United States
Duration: Nov 6 2011Nov 9 2011

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
CountryUnited States
CityPacific Grove, CA
Period11/6/1111/9/11

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