  In dynamic imaging, a key challenge is to reconstruct image sequences with
high temporal resolution from strong undersampling projections due to a
relatively slow data acquisition speed. In this paper, we propose a variational
model using the infimal convolution of Bregman distance with respect to total
variation to model edge dependence of sequential frames. The proposed model is
solved via an alternating iterative scheme, for which each subproblem is convex
and can be solved by existing algorithms. The proposed model is formulated
under both Gaussian and Poisson noise assumption and the simulation on two sets
of dynamic images shows the advantage of the proposed method compared to
previous methods.
