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pro vyhledávání: '"Alkhouri"'
Deep learning (DL) methods have been extensively applied to various image recovery problems, including magnetic resonance imaging (MRI) and computed tomography (CT) reconstruction. Beyond supervised models, other approaches have been recently explore
Externí odkaz:
http://arxiv.org/abs/2410.04482
Autor:
Alkhouri, Ismail, Liang, Shijun, Huang, Cheng-Han, Dai, Jimmy, Qu, Qing, Ravishankar, Saiprasad, Wang, Rongrong
Diffusion models (DMs) are a class of generative models that allow sampling from a distribution learned over a training set. When applied to solving inverse imaging problems (IPs), the reverse sampling steps of DMs are typically modified to approxima
Externí odkaz:
http://arxiv.org/abs/2410.04479
Autor:
Alkhouri N; University College London Hospital, Royal National ENT and Eastman Dental Hospital, London, UK.
Publikováno v:
British dental journal [Br Dent J] 2022 Sep; Vol. 233 (5), pp. 401.
Autor:
Alkhouri, Ismail, Denmat, Cedric Le, Li, Yingjie, Yu, Cunxi, Liu, Jia, Wang, Rongrong, Velasquez, Alvaro
Combinatorial Optimization (CO) addresses many important problems, including the challenging Maximum Independent Set (MIS) problem. Alongside exact and heuristic solvers, differentiable approaches have emerged, often using continuous relaxations of R
Externí odkaz:
http://arxiv.org/abs/2406.19532
Deep Neural Networks (DNNs) have achieved remarkable success in addressing many previously unsolvable tasks. However, the storage and computational requirements associated with DNNs pose a challenge for deploying these trained models on resource-limi
Externí odkaz:
http://arxiv.org/abs/2405.03089
Diffusion models have recently gained traction as a powerful class of deep generative priors, excelling in a wide range of image restoration tasks due to their exceptional ability to model data distributions. To solve image restoration problems, many
Externí odkaz:
http://arxiv.org/abs/2403.06054
Diffusion models, emerging as powerful deep generative tools, excel in various applications. They operate through a two-steps process: introducing noise into training samples and then employing a model to convert random noise into new samples (e.g.,
Externí odkaz:
http://arxiv.org/abs/2312.09181
Autor:
Liang, Shijun, Nguyen, Van Hoang Minh, Jia, Jinghan, Alkhouri, Ismail, Liu, Sijia, Ravishankar, Saiprasad
As the popularity of deep learning (DL) in the field of magnetic resonance imaging (MRI) continues to rise, recent research has indicated that DL-based MRI reconstruction models might be excessively sensitive to minor input disturbances, including wo
Externí odkaz:
http://arxiv.org/abs/2312.07784
Autor:
Nabih Alkhouri
Publikováno v:
British Dental Journal. 233:401-401
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9be4f5f4c0056b4470a3debf1b121bae
https://doi.org/10.1038/s41415-022-5002-3
https://doi.org/10.1038/s41415-022-5002-3