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AuthorQayyum, Adnan
AuthorIlahi, Inaam
AuthorShamshad, Fahad
AuthorBoussaid, Farid
AuthorBennamoun, Mohammed
AuthorQadir, Junaid
Available date2023-07-13T05:40:52Z
Publication Date2023
Publication NameIEEE Transactions on Pattern Analysis and Machine Intelligence
ResourceScopus
ISSN1628828
URIhttp://dx.doi.org/10.1109/TPAMI.2022.3204527
URIhttp://hdl.handle.net/10576/45577
AbstractIn recent years, advancements in machine learning (ML) techniques, in particular, deep learning (DL) methods have gained a lot of momentum in solving inverse imaging problems, often surpassing the performance provided by hand-crafted approaches. Traditionally, analytical methods have been used to solve inverse imaging problems such as image restoration, inpainting, and superresolution. Unlike analytical methods for which the problem is explicitly defined and the domain knowledge is carefully engineered into the solution, DL models do not benefit from such prior knowledge and instead make use of large datasets to predict an unknown solution to the inverse problem. Recently, a new paradigm of training deep models using a single image, named untrained neural network prior (UNNP) has been proposed to solve a variety of inverse tasks, e.g., restoration and inpainting. Since then, many researchers have proposed various applications and variants of UNNP. In this paper, we present a comprehensive review of such studies and various UNNP applications for different tasks and highlight various open research problems which require further research. 1979-2012 IEEE.
SponsorOpen Access funding provided by the Qatar National Library.
Languageen
PublisherIEEE Computer Society
Subjectdeep learning
Inverse imaging problems
untrained neural networks priors
TitleUntrained Neural Network Priors for Inverse Imaging Problems: A Survey
TypeArticle
Pagination6511-6536
Issue Number5
Volume Number45
dc.accessType Open Access


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