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9 September 2019 Wavelet/shearlet hybridized neural networks for biomedical image restoration
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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.
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Bart Goossens, Hiêp Luong, and Wilfried Philips "Wavelet/shearlet hybridized neural networks for biomedical image restoration", Proc. SPIE 11138, Wavelets and Sparsity XVIII, 111380H (9 September 2019); https://doi.org/10.1117/12.2530684
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