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

Gabriele Scrivanti 1 Luca Calatroni 2 Serena Morigi 1 Lindsay Nicholson 3 Alin Achim 3
2 MORPHEME - Morphologie et Images
CRISAM - Inria Sophia Antipolis - Méditerranée , IBV - Institut de Biologie Valrose : U1091, Laboratoire I3S - SIS - Signal, Images et Systèmes
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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Contributor : Luca Calatroni <>
Submitted on : Monday, October 26, 2020 - 10:54:08 AM
Last modification on : Friday, October 30, 2020 - 12:04:04 PM

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  • HAL Id : hal-02978016, version 1
  • ARXIV : 2010.12576



Gabriele Scrivanti, Luca Calatroni, Serena Morigi, Lindsay Nicholson, Alin Achim. Non-convex Super-resolution of OCT images via sparse representation. 2020. ⟨hal-02978016⟩



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