  In recent investigations, the problem of detecting edges given non-uniform
Fourier data was reformulated as a sparse signal recovery problem with an
l1-regularized least squares cost function. This result can also be derived by
employing a Bayesian formulation. Specifically, reconstruction of an edge map
using l1 regularization corresponds to a so-called type-I (maximum a
posteriori) Bayesian estimate. In this paper, we use the Bayesian framework to
design an improved algorithm for detecting edges from non-uniform Fourier data.
In particular, we employ what is known as type-II Bayesian estimation,
specifically a method called sparse Bayesian learning. We also show that our
new edge detection method can be used to improve downstream processes that rely
on accurate edge information like image reconstruction, especially with regards
to compressed sensing techniques.
