Modelling the mean and variability in a population of images, a task referred to as atlas estimation, remains very challenging, especially in a clinical setting where deformations between images can occur at multiple scales. In this paper, we introduce a coarse-to-fine strategy for atlas estimation in the Large Deformation Diffeomorphic Metric Mapping framework, based on a finite parametrization of the subjects' velocity field. Using the Haar Wavelet Transform, a multiscale representation of the initial velocity fields is computed in order to optimize the template-to-subject deformations in a coarse-to-fine fashion. This reparametrization preserves the reproducing kernel Hilbert space structure of the velocity fields, enabling the algorithm to perform efficiently gradient descent. Numerical experiments on three different datasets, including a dataset of abnormal fetal brain images, show that compared to the original algorithm, the coarse-to-fine strategy reaches higher performance and yields template images that preserve important details while avoiding unrealistic features.