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 ell-{1}-based reconstructions show the better performance of non-convex models, especially in view of further OCT image analysis.

Non-convex super-resolution of oct images via sparse representation

Calatroni L.;
2021-01-01

Abstract

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 ell-{1}-based reconstructions show the better performance of non-convex models, especially in view of further OCT image analysis.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1229279
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