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We propose a non-convex variational model for the super-resolution of OpticalCoherence Tomography (OCT) images of the murine eye, by enforcing sparsity withrespect to suitable dictionaries learnt from high-resolution OCT data. Thestatistical characteristics of OCT images motivate the use of {\alpha}-stabledistributions 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 problemparticularly challenging. We propose an efficient algorithm for minimizing thefunction based on the forward-backward splitting strategy which guarantees ateach iteration the existence and uniqueness of the proximal point. Comparisonswith standard convex L1-based reconstructions show the better performance ofnon-convex models, especially in view of further OCT image analysis

Non-convex Super-resolution of OCT images via sparse representation