  We propose a non-convex variational model for the super-resolution of Optical
Coherence Tomography (OCT) images of the murine eye, by enforcing sparsity with
respect to suitable dictionaries learnt from high-resolution OCT data. The
statistical characteristics of OCT images motivate the use of {\alpha}-stable
distributions for learning dictionaries, by considering the non-Gaussian case,
{\alpha}=1. The sparsity-promoting cost function relies on a non-convex penalty
- Cauchy-based or Minimax Concave Penalty (MCP) - which makes the problem
particularly challenging. We propose an efficient algorithm for minimizing the
function based on the forward-backward splitting strategy which guarantees at
each iteration the existence and uniqueness of the proximal point. Comparisons
with standard convex L1-based reconstructions show the better performance of
non-convex models, especially in view of further OCT image analysis
