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Wavelet/shearlet hybridized neural networks for biomedical image restoration
Author(s): Bart Goossens; Hiêp Luong; Wilfried Philips
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Paper Abstract

Recently, new programming paradigms have emerged that combine parallelism and numerical computations with algorithmic differentiation. This approach allows for the hybridization of neural network techniques for inverse imaging problems with more traditional methods such as wavelet-based sparsity modelling techniques. The benefits are twofold: on the one hand traditional methods with well-known properties can be integrated in neural networks, either as separate layers or tightly integrated in the network, on the other hand, parameters in traditional methods can be trained end-to-end from datasets in a neural network "fashion" (e.g., using Adagrad or Adam optimizers). In this paper, we explore these hybrid neural networks in the context of shearlet-based regularization for the purpose of biomedical image restoration. Due to the reduced number of parameters, this approach seems a promising strategy especially when dealing with small training data sets.

Paper Details

Date Published: 9 September 2019
PDF: 12 pages
Proc. SPIE 11138, Wavelets and Sparsity XVIII, 111380H (9 September 2019); doi: 10.1117/12.2530684
Show Author Affiliations
Bart Goossens, Ghent Univ. (Belgium)
Hiêp Luong, Ghent Univ. (Belgium)
Wilfried Philips, Ghent Univ. (Belgium)


Published in SPIE Proceedings Vol. 11138:
Wavelets and Sparsity XVIII
Dimitri Van De Ville; Manos Papadakis; Yue M. Lu, Editor(s)

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