Performance Bounds on Sparse Representations Using Redundant Frames

Abstract

We consider approximations of signals by the elements of a frame in a complex vector space of dimension $N$ and formulate both the noiseless and the noisy sparse representation problems. The noiseless representation problem is to find sparse representations of a signal $\mathbf{r}$ given that such representations exist. In this case, we explicitly construct a frame, referred to as the Vandermonde frame, for which the noiseless sparse representation problem can be solved uniquely using $O(N^2)$ operations, as long as the number of non-zero coefficients in the sparse representation of $\mathbf{r}$ is $εN$ for some $0 \le ε\le 0.5$, thus improving on a result of Candes and Tao \cite{Candes-Tao}. We also show that $ε\le 0.5$ cannot be relaxed without violating uniqueness. The noisy sparse representation problem is to find sparse representations of a signal $\mathbf{r}$ satisfying a distortion criterion. In this case, we establish a lower bound on the trade-off between the sparsity of the representation, the underlying distortion and the redundancy of any given frame.