Deep learning methods for solving linear inverse problems: Research directions and paradigms

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dc.contributor.authorBai, Yanna
dc.contributor.authorChen, Wei
dc.contributor.authorChen, Jie
dc.contributor.authorGuo, Weisi
dc.date.accessioned2020-12-11T17:15:12Z
dc.date.available2020-12-11T17:15:12Z
dc.date.issued2020-08-07
dc.description.abstractThe linear inverse problem is fundamental to the development of various scientific areas. Innumerable attempts have been carried out to solve different variants of the linear inverse problem in different applications. Nowadays, the rapid development of deep learning provides a fresh perspective for solving the linear inverse problem, which has various well-designed network architectures results in state-of-the-art performance in many applications. In this paper, we present a comprehensive survey of the recent progress in the development of deep learning for solving various linear inverse problems. We review how deep learning methods are used in solving different linear inverse problems, and explore the structured neural network architectures that incorporate knowledge used in traditional methods. Furthermore, we identify open challenges and potential future directions along this research line.en_UK
dc.identifier.citationBai Y, Chen W, Chen J, Guo W. (2020) Deep learning methods for solving linear inverse problems: Research directions and paradigms. Signal Processing, Volume 177, December 2020, Article number 107729en_UK
dc.identifier.issn0165-1684
dc.identifier.urihttps://doi.org/10.1016/j.sigpro.2020.107729
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16086
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDeep learningen_UK
dc.subjectLinear inverse problemsen_UK
dc.subjectNeural networksen_UK
dc.titleDeep learning methods for solving linear inverse problems: Research directions and paradigmsen_UK
dc.typeArticleen_UK

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