  This paper investigates two parameters that measure the coherence of a frame:
worst-case and average coherence. We first use worst-case and average coherence
to derive near-optimal probabilistic guarantees on both sparse signal detection
and reconstruction in the presence of noise. Next, we provide a catalog of
nearly tight frames with small worst-case and average coherence. Later, we find
a new lower bound on worst-case coherence; we compare it to the Welch bound and
use it to interpret recently reported signal reconstruction results. Finally,
we give an algorithm that transforms frames in a way that decreases average
coherence without changing the spectral norm or worst-case coherence.
