Compressed sensing for fusion frames

Petros Boufounos; Gitta Kutyniok; Holger Rauhut

doi:10.1117/12.826327

4 September 2009 Compressed sensing for fusion frames

Petros Boufounos, Gitta Kutyniok, Holger Rauhut

Proceedings Volume 7446, Wavelets XIII; 744614 (2009) https://doi.org/10.1117/12.826327
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States

Abstract

Compressed Sensing (CS) is a new signal acquisition technique that allows sampling of sparse signals using significantly fewer measurements than previously thought possible. On the other hand, a fusion frame is a new signal representation method that uses collections of subspaces instead of vectors to represent signals. This work combines these exciting new fields to introduce a new sparsity model for fusion frames. Signals that are sparse under the new model can be compressively sampled and uniquely reconstructed in ways similar to sparse signals using standard CS. The combination provides a promising new set of mathematical tools and signal models useful in a variety of applications. With the new model, a sparse signal has energy in very few of the subspaces of the fusion frame, although it needs not be sparse within each of the subspaces it occupies. We define a mixed ℓ₁/ℓ₂ norm for fusion frames. A signal sparse in the subspaces of the fusion frame can thus be sampled using very few random projections and exactly reconstructed using a convex optimization that minimizes this mixed ℓ₁/ℓ₂ norm. The sampling conditions we derive are very similar to the coherence and RIP conditions used in standard CS theory.

Citation Download Citation

Petros Boufounos, Gitta Kutyniok, and Holger Rauhut "Compressed sensing for fusion frames", Proc. SPIE 7446, Wavelets XIII, 744614 (4 September 2009); https://doi.org/10.1117/12.826327

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