Recovery of exact sparse representations in the presence of bounded noise

• Authors: J. J. Fuchs
• Venue: IEEE Transactions on Information Theory
• Year: 2005

The purpose of this contribution is to extend some recent results on sparse representations of signals in redundant bases developed in the noise-free case to the case of noisy observations. The type of question addressed so far is as follows: given an (n,m)-matrix A with m>n and a vector b=Axo, i.e., admitting a sparse representation xo, find a sufficient condition for b to have a unique sparsest representation. The answer is a bound on the number of nonzero entries in xo. We consider the case b=Axo+e where xo satisfies the sparsity conditions requested in the noise-free case and e is a vector of additive noise or modeling errors, and seek conditions under which xo can be recovered from b in a sense to be defined. The conditions we obtain relate the noise energy to the signal level as well as to a parameter of the quadratic program we use to recover the unknown sparsest representation. When the signal-to-noise ratio is large enough, all the components of the signal are still present when the noise is deleted; otherwise, the smallest components of the signal are themselves erased in a quite rational and predictable way