# Sparse Recovery from Combined Fusion Frame Measurements

## TL;DR

This paper introduces a new sparsity model for fusion frames that enables efficient compressed sensing and exact reconstruction of signals sparse across subspaces, expanding the mathematical tools for signal processing.

## Contribution

It proposes a novel sparsity model for fusion frames using a mixed l1/l2 norm, allowing for compressive sampling and exact recovery of signals with fewer measurements.

## Key findings

- Sampling conditions generalize coherence and RIP in CS
- Exact reconstruction guaranteed under new conditions
- Recovery probability improves exponentially with subspace dimension

## Abstract

Sparse representations have emerged as a powerful tool in signal and information processing, culminated by the success of new acquisition and processing techniques such as Compressed Sensing (CS). Fusion frames are very rich new signal representation methods that use collections of subspaces instead of vectors to represent signals. This work combines these exciting 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 does not need to be sparse within each of the subspaces it occupies. This sparsity model is captured using a mixed l1/l2 norm for fusion frames. A signal sparse in a fusion frame can be sampled using very few random projections and exactly reconstructed using a convex optimization that minimizes this mixed l1/l2 norm. The provided sampling conditions generalize coherence and RIP conditions used in standard CS theory. It is demonstrated that they are sufficient to guarantee sparse recovery of any signal sparse in our model. Moreover, a probabilistic analysis is provided using a stochastic model on the sparse signal that shows that under very mild conditions the probability of recovery failure decays exponentially with increasing dimension of the subspaces.