This paper deals with super-resolution, i.e. the reconstruction of a high resolution image from a sequence of low resolution noisy and possibly blurred images. We have developed an iterative procedure for minimizing a measure of discrepancy based on the Csiszàr’s I-divergence. One advantage of this algorithm is to provide a natural positivity constraint on the solution. We consider a block-based version to speed up convergence and we propose a computationally efficient implementation. We also introduce a temporal multiscale version of the algorithm, which proves to be more robust and stable. The algorithm requires the computation of the apparent motion in the image sequence. We use a robust multiresolution estimator of a 2D parametric motion model in order to keep computational efficiency.