The concept of deep learning is employed for the inversion of NMR signals and it is shown that NMR signal inversion can be considered as an image-to-image regression problem, which can be treated with a convolutional neural net. It is further outlined, that inversion through deep learning provides a clear efficiency and usability advantage compared to regularization techniques such as Tikhonov and modified total generalized variation (MTGV), because no hyperparemeter selection prior to reconstruction is necessary. The inversion network is applied to simulated NMR signals and the results compared with Tikhonov- and MTGV-regularization. The comparison shows that inversion via deep learning is significantly faster than the latter regularization methods and also outperforms both regularization techniques in nearly all instances.
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