Zobrazeno 1 - 10
of 164
pro vyhledávání: '"Calatroni, Luca"'
Off-the-grid regularisation has been extensively employed over the last decade in the context of ill-posed inverse problems formulated in the continuous setting of the space of Radon measures $\mathcal{M}(\mathcal{X})$. These approaches enjoy convexi
Externí odkaz:
http://arxiv.org/abs/2404.00810
We propose an unfolded accelerated projected-gradient descent procedure to estimate model and algorithmic parameters for image super-resolution and molecule localization problems in image microscopy. The variational lower-level constraint enforces sp
Externí odkaz:
http://arxiv.org/abs/2403.17506
We consider an unsupervised bilevel optimization strategy for learning regularization parameters in the context of imaging inverse problems in the presence of additive white Gaussian noise. Compared to supervised and semi-supervised metrics relying e
Externí odkaz:
http://arxiv.org/abs/2403.07026
We propose a new class of exact continuous relaxations of l0-regularized criteria involving non-quadratic data terms such as the Kullback-Leibler divergence and the logistic regression, possibly combined with an l2 regularization. We first prove the
Externí odkaz:
http://arxiv.org/abs/2402.06483
Autor:
Aujol, Jean-François, Calatroni, Luca, Dossal, Charles, Labarrière, Hippolyte, Rondepierre, Aude
We consider a combined restarting and adaptive backtracking strategy for the popular Fast Iterative Shrinking-Thresholding Algorithm frequently employed for accelerating the convergence speed of large-scale structured convex optimization problems. Se
Externí odkaz:
http://arxiv.org/abs/2307.14323
A novel numerical strategy is introduced for computing approximations of solutions to a Cahn-Hilliard model with degenerate mobilities. This model has recently been introduced as a second-order phase-field approximation for surface diffusion flows. I
Externí odkaz:
http://arxiv.org/abs/2306.15329
Autor:
Merizzi, Fabio, Saillard, Perrine, Acquier, Oceane, Morotti, Elena, Piccolomini, Elena Loli, Calatroni, Luca, Dessì, Rosa Maria
The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disciplines. In the field of digital humanities, the task of digital recon
Externí odkaz:
http://arxiv.org/abs/2306.14209
The spatial resolution of images of living samples obtained by fluorescence microscopes is physically limited due to the diffraction of visible light, which makes the study of entities of size less than the diffraction barrier (around 200 nm in the x
Externí odkaz:
http://arxiv.org/abs/2303.11212
We consider a stochastic gradient descent (SGD) algorithm for solving linear inverse problems (e.g., CT image reconstruction) in the Banach space framework of variable exponent Lebesgue spaces $\ell^{(p_n)}(\mathbb{R})$. Such non-standard spaces have
Externí odkaz:
http://arxiv.org/abs/2303.09182