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pro vyhledávání: '"P Marien"'
Plug-and-Play methods for image restoration are iterative algorithms that solve a variational problem to restore an image. These algorithms are known to be flexible to changes of degradation and to perform state-of-the-art restoration. Recently, a lo
Externí odkaz:
http://arxiv.org/abs/2412.08262
One key ingredient of image restoration is to define a realistic prior on clean images to complete the missing information in the observation. State-of-the-art restoration methods rely on a neural network to encode this prior. Moreover, typical image
Externí odkaz:
http://arxiv.org/abs/2412.05343
Empirically, it has been observed that adding momentum to Stochastic Gradient Descent (SGD) accelerates the convergence of the algorithm. However, the literature has been rather pessimistic, even in the case of convex functions, about the possibility
Externí odkaz:
http://arxiv.org/abs/2410.07870
The recent rise of deep learning has led to numerous applications, including solving partial differential equations using Physics-Informed Neural Networks. This approach has proven highly effective in several academic cases. However, their lack of ph
Externí odkaz:
http://arxiv.org/abs/2410.02819
In this work we address the problem of finding serendipity versions of approximate de Rham complexes with enhanced regularity. The starting point is a new abstract construction of general scope which, given three complexes linked by extension and red
Externí odkaz:
http://arxiv.org/abs/2407.12625
We report on the experimental observation of non-resonant, second-order optical Sum-Frequency Generation (SFG) in five different atomic and molecular gases. The measured signal is attributed to a SFG process by characterizing its intensity scaling an
Externí odkaz:
http://arxiv.org/abs/2403.18161
Plug-and-Play (PnP) algorithms are a class of iterative algorithms that address image inverse problems by combining a physical model and a deep neural network for regularization. Even if they produce impressive image restoration results, these algori
Externí odkaz:
http://arxiv.org/abs/2402.01779
We design in this work a discrete de Rham complex on manifolds. This complex, written in the framework of exterior calculus, is applicable on meshes on the manifold with generic elements, and has the same cohomology as the continuous de Rham complex.
Externí odkaz:
http://arxiv.org/abs/2401.16130
Autor:
Morell-Ortega, Sergio, Ruiz-Perez, Marina, Gadea, Marien, Vivo-Hernando, Roberto, Rubio, Gregorio, Aparici, Fernando, de la Iglesia-Vaya, Maria, Catheline, Gwenaelle, Coupé, Pierrick, Manjón, José V.
This paper introduces a novel multimodal and high-resolution human brain cerebellum lobule segmentation method. Unlike current tools that operate at standard resolution ($1 \text{ mm}^{3}$) or using mono-modal data, the proposed method improves cereb
Externí odkaz:
http://arxiv.org/abs/2401.12074
Publikováno v:
Civil-Comp Conferences, Volume 5, Paper 4.2, Civil-Comp Press, Edinburgh, United Kingdom, 2023
Since the seminal work of [9] and their Physics-Informed neural networks (PINNs), many efforts have been conducted towards solving partial differential equations (PDEs) with Deep Learning models. However, some challenges remain, for instance the exte
Externí odkaz:
http://arxiv.org/abs/2310.14948