Cosparse Analysis Modeling

Abstract : In the past decade there has been a great interest in a synthesis-based model for signals, based on sparse and redundant representations. Such a model assumes that the signal of interest can be composed as a linear combination of few columns from a given matrix (the dictionary). An alternative analysis-based model can be envisioned, where an analysis operator multiplies the signal, leading to a cosparse outcome. In this paper, we consider this analysis model, in the context of a generic missing data problem (e.g., compressed sensing, inpainting, source separation, etc.). Our work proposes a uniqueness result for the solution of this problem, based on properties of the analysis operator and the measurement matrix. This paper also considers two pursuit algorithms for solving the missing data problem, an L1-based and a new greedy method. Our simulations demonstrate the appeal of the analysis model, and the success of the pursuit techniques presented. In this work, we consider what we call cosparse analysis model, in the context of a generic missing data problem (e.g., compressed sensing, inpainting, source separation, etc.). Our work proposes a uniqueness result for the solution of this problem and considers algorithms to solving it.
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Submitted on : Friday, September 16, 2011 - 11:10:34 AM
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Sangnam Nam, Mike E. Davies, Michael Elad, Rémi Gribonval. Cosparse Analysis Modeling. The 9th International Conference on Sampling Theory and Applications, May 2011, Singapore, Singapore. ⟨inria-00591779⟩

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