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Sparse Modeling and Compressive Sensing

Sparse modeling and compressive sensing are novel methods for signal representation and acquisition. Sparse signal representations are manifestation of the parsimony principle also known as the Occam’s razor which states that the simplest and most concise explanation of a natural phenomenon is in most cases the best one possible. Sparse structure appears to be an inherent property of many natural signals when observed in an appropriate basis. Compressive sensing represents a signal acquisition technique that exploits underlying sparse signal structure and enables accurate signal recovery from an incomplete set of measurements. In this overview, basis representation and sparse decomposition fundamentals and sparse recovery problem formulations are covered and bridged with different practical applications of compressive sensing framework. The most recent advances in the compressive sensing theory and the state- of-theart applications are presented.

sparse modeling, compressive sensing, basis representation, signal processing, signal acquisition, optimization

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