SubmissionPapersProceedingsConferences

New Submissions↑

  • A. Benfenati, A. Marta, A singular Riemannian Geometry Approach to Deep Neural Networks III. Piecewise Differentiable Layers and Random Walks on n-dimensional Classes. Preprint
  • A. Benfenati, P. Causin, M. Quinteri, A Modular Deep Learning-based Approach for Diffuse Optical Tomography Reconstruction, preprint.

  • A. Benfenati, E. Chouzenoux, G. Franchini, S. Latva-Aijo, D. Narnhofer, J.-C. Pesquet, S J. Scott, M Yousefi, Majorization-Minimization for sparse SVMs, accepted for publication. Preprint

  • E. N. Frola, A. Cavaliere, E. De Marchi, F. D’Alessandro, A. Benfenati, G. Aletti, A. Banterle, Water scarcity and consumer behaviour: an analysis of diet-related Water Footprint

Papers↑

  • A. Benfenati, P. Cascarano, Constrained Plug and Play Priors for Image Restoration, Journal of Imaging. Link

  • P. Cascarano, A. Benfenati, U. S. Kamilov, X. Xu, Constrained Regularization by Denoising with Automatic Parameter Selection, IEEE Signal Processing Letters. Link.

  • A. Aspri, A. Benfenati, P. Causin, C. Cavaterra and G. Naldi, Mathematical and numerical challenges in Diffuse Optical Tomography inverse problems. Link.

  • Benfenati, A., Causin, P., Oberti, R., G. Stefanello, Unsupervised deep learning techniques for automatic detection of plant diseases: reducing the need of manual labelling of plant images. J.Math.Industry. Link

  • A. Cavaliere, E. De Marchi, E. N. Frola, A. Benfenati, G. Aletti, J. Bacenetti, A. Banterle, Exploring environmental externalities associated with deviations from the Mediterranean 2 Diet, and how to reduce diet-related environmental impact. Link.

  • A. Benfenati, A. Catozzi, V. Ruggiero, Neural Blind Deconvolution with Poisson Data. Inverse Problems. Link

  • A. Benfenati, A. Marta, A singular Riemannian geometry approach to Deep Neural Networks I. Theoretical foundations. Neural Networks. Link.

  • A. Benfenati, A. Marta, A singular Riemannian geometry approach to Deep Neural Networks II. Reconstruction of 1-D equivalence classes. Neural Networks. Link

  • A. Benfenati, D. Bolzi, P. Causin, R. Oberti, A Deep Learning Generative Model Approach for Image Synthesis of Plant Leaves. PLOS ONE. Link.

  • A. Benfenati, G. Borghi, L. Pareschi. Binary interaction methods for high dimensional global optimization and machine learning. Applied Mathematics & Optimization. Link

  • A. Benfenati, upU-Net Approaches for Background Emission Removal in Fluorescence Microscopy, Journal of Imaging. Link

  • G. Aletti, A. Benfenati, G. Naldi. A New Nonlocal Nonlinear Diffusion Equation: the one-Dimensional Case, Bulletin of the Australian Mathematical Society. Link

  • G. Aletti, A. Benfenati, G. Naldi. A Semi-supervised Multilabel Segmentation Method for Hyperspectral images. Journal of Imaging. Article

  • G. Aletti, A. Benfenati, G. Naldi. Graph, spectra, control and epidemics: an example with a SEIR model, Mathematics. Article

  • G. Aletti, A. Benfenati, G. Naldi. A Semiautomatic Multi-Label Color Image Segmentation Coupling Dirichlet Problem and Colour Distances, Journal of Imaging. Article

  • A. Benfenati, E. Chouzenoux, and J.-C. Pesquet. Proximal Approaches for Matrix Optimization Problems: Application to Robust Precision Matrix Estimation, Signal Processing. Article

  • A. Benfenati, F. Bonacci, T. Bourouina, H. Talbot. Efficient segmentation and positioning of 3D fluorescent spherical beads in confocal microscopy, Journal of Mathematical Imaging and Vision. Article

  • A. Benfenati, E. Chouzenoux, L. Duval, J.-C. Pesquet, A. Pirayre. A review on graph optimization and algorithmic frameworks. HAL Archives, 2018. pdf

  • S. Bonettini, A. Benfenati, and V. Ruggiero. Scaling techniques for epsilon-subgradient methods. SIAM Journal on Optimization, 26(3):1741-1772, 2016. Article

  • A. Benfenati, A. La Camera, and M. Carbillet. Deconvolution of post-adaptive optics images of faint circumstellar environments by means of the inexact Bregman procedure. Astronomy & Astrophysics, 586:A16, 2016. Article

  • L. Zanni, A. Benfenati, M. Bertero, and V. Ruggiero. Numerical methods for parameter estimation in Poisson data inversion. Journal of Mathematical Imaging and Vision, 52(3):397-413, 2015. Article

  • A. Benfenati and V. Ruggiero. Inexact Bregman iteration for deconvolution of superimposed extended and point sources. Communications in Nonlinear Science and Numerical Simulation, 21(1):210-224, 2015. Article

  • A. Benfenati and V. Coscia. Modeling opinion formation in the kinetic theory of active particles I: spontaneous trend. Annali dell’Università di Ferrara, 60(1):35-53, 2014. Article

  • A.Benfenati and V.Ruggiero.Inexact Bregman iteration with an application to Poisson data reconstruction. Inverse Problems, 29(6):065016, 2013. Article

  • A. Benfenati and V. Coscia. Nonlinear microscale interactions in the kinetic theory of active particles. Applied Mathematics Letters, 26(10):979-983, 2013. Article

Proceedings↑

  • A. Benfenati, A. Catozzi, G. Franchini and F. Porta, Piece-wise Constant Image Segmentation with a Deep Image Prior Approach, Scale Space and Variational Methods in Computer Vision. SSVM 2023. Lecture Notes in Computer Science, vol 14009. Link

  • A. Benfenati, P. Causin,, M.G. Lupieri and G. Naldi, Regularization Techniques for Inverse Problem in DOT Applications, Journal of Physics: Conference Series, 1476, 2020, 012007 pdf

  • É. Puybareau, E. Carlinet, A. Benfenati and H. Talbot, Spherical Fluorescent Particle Segmentation and Tracking in 3D Confocal Microscopy, Mathematical Morphology and Its Applications to Signal and Image Processing (ISMM) 2019. Lecture Notes in Computer Science, vol 11564. Springer, Cham. pdf

  • A. Benfenati, E. Chouzenoux, and J.-C. Pesquet. A Nonconvex Variational Approach for Robust Graphical Lasso.. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018. pdf

  • A. Benfenati, E. Chouzenoux, and J.-C. Pesquet. A proximal approach for solving matrix optimization problems involving a Bregman divergence. In Proceedings of the International Biomedical and Astronomical Signal Processing Frontiers workshop (BASP), 2017. pdf

  • Benfenati, V Ruggiero, Image regularization for Poisson data , Journal of Physics: Conference Series, 657, 2015, 012011, pdf

  • S. Bonettini, A. Benfenati, and V. Ruggiero. Primal-dual first order methods for total variation image restoration in presence of Poisson noise. In 2014 IEEE International Conference on Image Processing (ICIP), pages 4156-4160, 2014. link

Attended Conferences and Workshops ↑

  • 21-23 May 2023, Piece-wise Constant Image Segmentation with a Deep Image Prior Approach, 9th International Conference Scale Space and Variational Methods in Computer Vision, Santa Margherita di Pula, Italy

  • 29-30 September 2022, LearnedSVD Approach for Diffuse Optical Tomography: Classical and Deep Learning Techniques, GIMC SIMAI YOUNG 2022, Pavia, Italy

  • 11-13 October 2021, A Local-Global Graph Approach for Coloured Image Segmentation, PRIMO Workshop, Bologna

  • 30 August – 3 Settember 2021, Organizer of the Mini Simposium Inverse Problems: New Frontiers in Bio–Medical Imaging, SIMAI2020-2021, Parma, Italy

  • 1-5 March 2020, Deep–Learning Based Regularization for DOT Inverse Problems, SIAM Conference on Computational Science and Engineering (CSE21), Virtual Conference

  • 18 June 2019, Proximal Approaches for Matrix Estimation Problems (talk), NUMTA 2019, Le Castella, Italy;

  • 24 May 2019, Regularization Techniques for Inverse Problem in DOT Applications, NCMIP 2019, Cachan, France

  • 10 May 2019, On a scaled epsilon-subgradient methods (talk), TiciNUM 2019, Pavia, Italy;

  • 15-20 April 2018, A Nonconvex Variational Approach for Robust Graphical LASSO (talk), IEEE ICASSP 2018, Calgary, Alberta, Canada;

  • 22 May-25 May 2017, On a Scaled epsilon-subgradient Method with Adaptive Stepsize Rule (talk), SIAM Conference on Optimization 2017, Vancouver, British Columbia, Canada;

  • 29 January – 3 February 2017, A Proximal Approach for Solving matrix Optimization Problems Involving a Bregman Divergence (poster), International BASP Frontiers workshop 2017, Heriot-Watt University, United Kingdom

  • 22 September 2016, Scaling techniques for epsilon-subgradient methods (poster), Optimization Techniques for Inverse Problems III, Università degli Studi di Modena e Reggio Emilia, Modena, Italy

  • 15 September 2016, A scaled epsilon-subgradient method (talk), SIMAI 2016, Politecnico di Milano, Milano, Italy

  • 29 May 2015, Image regularization for Poisson data (poster), 5th International Workshop on New Computational Methods for Inverse Problems, Institut Farman, Ecole Normale Sup erieure de Cachan, Cachan, France

  • 8-12 September 2014, Inexact Bregman Regularization for Poisson Data (talk), Optimization and dynamical processes in statistical learning and inverse problems, DIMA-Università degli studi di Genova, Genova, Italy

  • 7-10 July 2014, Inexact Bregman Regularization for Astronomical Images Corrupted by Poisson Noise (talk), SIMAI 2014, SIMAI

  • 26 June 2013 – 28 June 2013, Inexact Bregman Iteration with applications to Poisson data reconstruction (talk), 11th EUROPT Workshop on Advances in Continuous Optimization, Università degli Studi di Firenze, Firenze, Italy

  • 17 June 2013 – 23 June 2013, Image Restoration from Poisson Data by an Inexact Bregman Iteration Scheme (talk), Numerical Computations: Theory and Algorithms, Università della Calabria, with the collaboration of Lobachevsky State University, Italy

  • 31 January 2013 – 1 February 2013, Su un modello di formazione di opinioni: Considerazioni generali e primi risultati (talk), GA-MeMoMa- COMPLEX-SIMAI – Incontro plenario, Politecnico di Torino, Torino, Italy

  • 20 September 2012 – 21 September 2012, Image Restoration from Poisson data by Bregman Iteration (poster), Optimization Techniques for Inverse Problems II, Università degli studi di Modena e Reggio Emilia, Modena, Italy

  • 23 April 2012 – 27 April 2012, Image restoration of Poisson data with iterative Bregman regularization procedure (talk), Computational Inverse Problems, Erwin Schroedinger Institut, Vienna, Austria