  A new algorithm is developed to jointly recover a temporal sequence of images
from noisy and under-sampled Fourier data. Specifically, we consider the case
where each data set is missing vital information that prevents its (individual)
accurate recovery. Our new method is designed to restore the missing
information in each individual image by "borrowing" it from the other images in
the sequence. As a result, {\em all} of the individual reconstructions yield
improved accuracy. The use of high resolution Fourier edge detection methods is
essential to our algorithm. In particular, edge information is obtained
directly from the Fourier data which leads to an accurate coupling term between
data sets. Moreover, data loss is largely avoided as coarse reconstructions are
not required to process inter- and intra-image information. Numerical examples
are provided to demonstrate the accuracy, efficiency and robustness of our new
method.
