  This paper introduces a new sparse Bayesian learning (SBL) algorithm that
jointly recovers a temporal sequence of edge maps from noisy and under-sampled
Fourier data. The new method is cast in a Bayesian framework and uses a prior
that simultaneously incorporates intra-image information to promote sparsity in
each individual edge map with inter-image information to promote similarities
in any unchanged regions. By treating both the edges as well as the similarity
between adjacent images as random variables, there is no need to separately
form regions of change. Thus we avoid both additional computational cost as
well as any information loss resulting from pre-processing the image. Our
numerical examples demonstrate that our new method compares favorably with more
standard SBL approaches.
