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المؤلفQayyum, Adnan
المؤلفIlahi, Inaam
المؤلفShamshad, Fahad
المؤلفBoussaid, Farid
المؤلفBennamoun, Mohammed
المؤلفQadir, Junaid
تاريخ الإتاحة2023-07-13T05:40:52Z
تاريخ النشر2023
اسم المنشورIEEE Transactions on Pattern Analysis and Machine Intelligence
المصدرScopus
الرقم المعياري الدولي للكتاب1628828
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/TPAMI.2022.3204527
معرّف المصادر الموحدhttp://hdl.handle.net/10576/45577
الملخصIn 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.
راعي المشروعOpen Access funding provided by the Qatar National Library.
اللغةen
الناشرIEEE Computer Society
الموضوعdeep learning
Inverse imaging problems
untrained neural networks priors
العنوانUntrained Neural Network Priors for Inverse Imaging Problems: A Survey
النوعArticle
الصفحات6511-6536
رقم العدد5
رقم المجلد45
dc.accessType Open Access


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