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.File | Dimensione | Formato | |
---|---|---|---|
Non-Convex_Super-Resolution_Of_Oct_Images_Via_Sparse_Representation.pdf
accesso chiuso
Tipologia:
Documento in versione editoriale
Dimensione
3.48 MB
Formato
Adobe PDF
|
3.48 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.