Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Bubba, Tatiana A."'
We propose a variational regularization approach based on cylindrical shearlets to deal with dynamic imaging problems, with dynamic tomography as guiding example. The idea is that the mismatch term essentially integrates a sequence of separable, stat
Externí odkaz:
http://arxiv.org/abs/2405.06337
Source conditions are a key tool in regularisation theory that are needed to derive error estimates and convergence rates for ill-posed inverse problems. In this paper, we provide a recipe to practically compute source condition elements as the solut
Externí odkaz:
http://arxiv.org/abs/2303.00696
Autor:
Bubba, Tatiana A., Calatroni, Luca, Catozzi, Ambra, Crisci, Serena, Pock, Thomas, Pragliola, Monica, Rautio, Siiri, Riccio, Danilo, Sebastiani, Andrea
Bilevel learning is a powerful optimization technique that has extensively been employed in recent years to bridge the world of model-driven variational approaches with data-driven methods. Upon suitable parametrization of the desired quantities of i
Externí odkaz:
http://arxiv.org/abs/2302.10056
Limited-angle tomography is a highly ill-posed linear inverse problem. It arises in many applications, such as digital breast tomosynthesis. Reconstructions from limited-angle data typically suffer from severe stretching of features along the central
Externí odkaz:
http://arxiv.org/abs/2201.00656
Autor:
Bubba, Tatiana A., Ratti, Luca
Statistical inverse learning theory, a field that lies at the intersection of inverse problems and statistical learning, has lately gained more and more attention. In an effort to steer this interplay more towards the variational regularization frame
Externí odkaz:
http://arxiv.org/abs/2112.12443
Autor:
Bubba, Tatiana A., Easley, Glenn, Heikkilä, Tommi, Labate, Demetrio, Ayllon, Jose P. Rodriguez
Publikováno v:
Journal of Computational and Applied Mathematics 429 (2023) 115206
Efficient representations of multivariate functions are critical for the design of state-of-the-art methods of data restoration and image reconstruction. In this work, we consider the representation of spatio-temporal data such as temporal sequences
Externí odkaz:
http://arxiv.org/abs/2110.03221
Publikováno v:
21st International Conference on Computational Science and Its Applications (ICCSA), 2021, pp. 146-156
The dual-tree complex wavelet transform (DT-$\mathbb{C}$WT) is extended to the 4D setting. Key properties of 4D DT-$\mathbb{C}$WT, such as directional sensitivity and shift-invariance, are discussed and illustrated in a tomographic application. The i
Externí odkaz:
http://arxiv.org/abs/2103.15674
We consider a statistical inverse learning problem, where the task is to estimate a function $f$ based on noisy point evaluations of $Af$, where $A$ is a linear operator. The function $Af$ is evaluated at i.i.d. random design points $u_n$, $n=1,...,N
Externí odkaz:
http://arxiv.org/abs/2102.09526
Autor:
Virta, Riina, Backholm, Rasmus, Bubba, Tatiana A., Helin, Tapio, Moring, Mikael, Siltanen, Samuli, Dendooven, Peter, Honkamaa, Tapani
Safeguarding the disposal of spent nuclear fuel in a geological repository needs an effective, efficient, reliable and robust non-destructive assay (NDA) system to ensure the integrity of the fuel prior to disposal. In the context of the Finnish geol
Externí odkaz:
http://arxiv.org/abs/2009.11617
Autor:
Bubba, Tatiana A., Galinier, Mathilde, Lassas, Matti, Prato, Marco, Ratti, Luca, Siltanen, Samuli
We propose a novel convolutional neural network (CNN), called $\Psi$DONet, designed for learning pseudodifferential operators ($\Psi$DOs) in the context of linear inverse problems. Our starting point is the Iterative Soft Thresholding Algorithm (ISTA
Externí odkaz:
http://arxiv.org/abs/2006.01620