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Conference Papers Year : 2021

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

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 L1-based reconstructions show the better performance of non-convex models, especially in view of further OCT image analysis
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Dates and versions

hal-02978016 , version 1 (29-11-2021)

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Gabriele Scrivanti, Luca Calatroni, Serena Morigi, Lindsay Nicholson, Alin Achim. Non-convex Super-resolution of OCT images via sparse representation. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Apr 2021, Nice, France. ⟨hal-02978016⟩
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