{"id":27,"date":"2021-08-05T15:12:18","date_gmt":"2021-08-05T15:12:18","guid":{"rendered":"https:\/\/sites.unimi.it\/a_benfenati\/?page_id=27"},"modified":"2025-12-12T10:13:16","modified_gmt":"2025-12-12T10:13:16","slug":"publications","status":"publish","type":"page","link":"https:\/\/sites.unimi.it\/a_benfenati\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n<figure class=\"wp-block-table aligncenter is-style-regular\"><table class=\"has-fixed-layout\"><tbody><tr><td class=\"has-text-align-center\" data-align=\"center\"><a href=\"#news\">Submission<\/a><\/td><td class=\"has-text-align-center\" data-align=\"center\"><a href=\"#papers\">Papers<\/a><\/td><td class=\"has-text-align-center\" data-align=\"center\"><a href=\"#proceedings\">Proceedings<\/a><\/td><td class=\"has-text-align-center\" data-align=\"center\"><a href=\"#conf\">Conferences<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p style=\"font-size:30px\"><a name=\"news\">New Submissions<\/a><a href=\"https:\/\/sites.unimi.it\/a_benfenati\/publications\/\">\u2191<\/a><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>P. Cascarano, L. Stacchio,&nbsp;A. Sebastiani,&nbsp;A. Benfenati,&nbsp;U. S. Kamilov,&nbsp;G. Marfia, <em>RELD: Regularization by Latent Diffusion Models for Image Restoration<\/em><a rel=\"noreferrer noopener\" href=\"https:\/\/arxiv.org\/abs\/2503.22563\" data-type=\"URL\" data-id=\"https:\/\/arxiv.org\/abs\/2503.22563\" target=\"_blank\">. Preprint<\/a>, submitted.<\/li><\/ul>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, A. Marta, <em>A singular Riemannian Geometry Approach to Deep Neural Networks III. Piecewise Differentiable Layers and Random Walks on n-dimensional Classes<\/em>. <a rel=\"noreferrer noopener\" href=\"https:\/\/arxiv.org\/pdf\/2404.06104.pdf\" data-type=\"URL\" data-id=\"https:\/\/arxiv.org\/pdf\/2404.06104.pdf\" target=\"_blank\">Preprint<\/a><\/li><\/ul>\n\n\n\n<p style=\"font-size:30px\"><a name=\"papers\">Papers<\/a><a href=\"https:\/\/sites.unimi.it\/a_benfenati\/publications\/\">\u2191<\/a><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, <em>Plug and Play Splitting Techniques for Poisson Image Restoration<\/em>. Journal of Mathematical Imaging and Vision. <a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10851-025-01273-7\" data-type=\"URL\" data-id=\"https:\/\/link.springer.com\/article\/10.1007\/s10851-025-01273-7\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, L. Calatroni, P. Cascarano et al., <em>Variational Image Regularisation in the Era of Deep Learning: From Model-Based to Deep Priors and Back<\/em>, Journal of Mathematical Imaging and Vision. <a rel=\"noreferrer noopener\" href=\"https:\/\/link.springer.com\/article\/10.1007\/s10851-025-01272-8\" target=\"_blank\">Link<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Serianni, A. Benfenati, P. Causin, <em>Learnable Priors Support Reconstruction in Diffuse Optical Tomography<\/em>. Photonics. <a href=\"https:\/\/www.mdpi.com\/2304-6732\/12\/8\/746\" data-type=\"URL\" data-id=\"https:\/\/www.mdpi.com\/2304-6732\/12\/8\/746\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, A. Catozzi, G. Franchini, F. Porta, <em>Unsupervised noisy image segmentation using Deep Image Prior<\/em>, Mathematics and Computers in Simulation. <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S037847542500326X\" data-type=\"URL\" data-id=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S037847542500326X\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a>.<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, P. Causin, M. Quinteri, <em>A Modular Deep Learning-based Approach for Diffuse Optical Tomography Reconstruction<\/em>. <a rel=\"noreferrer noopener\" href=\"https:\/\/arxiv.org\/abs\/2402.09277\" target=\"_blank\">Link<\/a>,<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Serianni, A.Benfenati, P. Causin, <em>Learnable Priors Support Reconstruction in Diffuse Optical Tomography<\/em>. <a href=\"https:\/\/www.mdpi.com\/2304-6732\/12\/8\/746\" data-type=\"URL\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, A. Catozzi, G. Franchini, F. Porta, <em>Early stopping strategies in Deep Image Prior<\/em>. <a href=\"https:\/\/link.springer.com\/article\/10.1007\/s00500-025-10642-8\" data-type=\"URL\" data-id=\"https:\/\/link.springer.com\/article\/10.1007\/s00500-025-10642-8\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, E. Chouzenoux, G. Franchini, S. Latva-Aijo, D. Narnhofer, J.-C. Pesquet, S J. Scott, M Yousefi, <em>Majorization-Minimization for sparse SVMs<\/em>, accepted for publication. <a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-981-97-6769-4_3\" data-type=\"URL\" data-id=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-981-97-6769-4_3\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>E. N. Frola, A. Cavaliere, E. De Marchi, F. D&#8217;Alessandro, A. Benfenati, G. Aletti, A. Banterle, <em>Water scarcity and consumer behaviour: an analysis of diet-related Water Footprint<\/em>, <a href=\"https:\/\/www.cambridge.org\/core\/journals\/renewable-agriculture-and-food-systems\/article\/water-scarcity-and-consumer-behavior-an-analysis-of-dietrelated-water-footprint\/A85B9BDAE1CBFDE66AF1F9D7677EEA67\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, P. Cascarano, <em>Constrained Plug and Play Priors for Image Restoration<\/em>, Journal of Imaging. <a href=\"https:\/\/www.mdpi.com\/2313-433X\/10\/2\/50\" data-type=\"URL\" data-id=\"https:\/\/www.mdpi.com\/2313-433X\/10\/2\/50\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>P. Cascarano, A. Benfenati, U. S<a href=\"https:\/\/arxiv.org\/search\/math?searchtype=author&amp;query=Kamilov,+U+S\">. <\/a>Kamilov, X. Xu, <em>Constrained Regularization by Denoising with Automatic Parameter Selection<\/em>, IEEE Signal Processing Letters. <a rel=\"noreferrer noopener\" href=\"https:\/\/ieeexplore.ieee.org\/document\/10415495\" target=\"_blank\">Link<\/a>.<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Aspri, A. Benfenati, P. Causin, C. Cavaterra and G. Naldi, <em>Mathematical and numerical challenges in Diffuse Optical Tomography inverse problems<\/em>. <a rel=\"noreferrer noopener\" href=\"https:\/\/www.aimsciences.org\/article\/doi\/10.3934\/dcdss.2023210?viewType=HTML\" data-type=\"URL\" data-id=\"https:\/\/www.aimsciences.org\/article\/doi\/10.3934\/dcdss.2023210?viewType=HTML\" target=\"_blank\">Link<\/a>.<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Benfenati, A., Causin, P., Oberti, R., G. Stefanello, <em>Unsupervised deep learning techniques for automatic detection of plant diseases: reducing the need of manual labelling of plant images<\/em>. J.Math.Industry. <a rel=\"noreferrer noopener\" href=\"https:\/\/mathematicsinindustry.springeropen.com\/articles\/10.1186\/s13362-023-00133-6\" target=\"_blank\">Link<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Cavaliere, E. De Marchi, E. N. Frola, A. Benfenati, G. Aletti, J. Bacenetti, A. Banterle, <em>Exploring environmental externalities associated with deviations from the Mediterranean 2 Diet, and how to reduce diet-related environmental impact<\/em>. <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0921800923000812?via%3Dihub\" data-type=\"URL\" data-id=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0921800923000812?via%3Dihub\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a>.<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, A. Catozzi, V. Ruggiero, <em>Neural Blind Deconvolution with Poisson Data<\/em>. Inverse  Problems. <a rel=\"noreferrer noopener\" href=\"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6420\/acc2e0\" data-type=\"URL\" data-id=\"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6420\/acc2e0\" target=\"_blank\">Link<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, A. Marta, <em>A singular Riemannian geometry approach to Deep Neural Networks I. Theoretical foundations<\/em>. Neural Networks. <a rel=\"noreferrer noopener\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0893608022004634\" target=\"_blank\">Link<\/a>.<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, A. Marta, <em>A singular Riemannian geometry approach to Deep Neural Networks II. Reconstruction of 1-D equivalence classes<\/em>. Neural Networks. <a rel=\"noreferrer noopener\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0893608022004671\" target=\"_blank\">Link<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, D. Bolzi, P. Causin, R. Oberti, <em>A Deep Learning Generative Model Approach for Image Synthesis of Plant Leaves.<\/em> PLOS ONE. <a rel=\"noreferrer noopener\" href=\"https:\/\/journals.plos.org\/plosone\/article?id=10.1371\/journal.pone.0276972\" target=\"_blank\">Link<\/a>. <\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, G. Borghi, L. Pareschi. <em>Binary interaction methods for high dimensional global optimization and machine learning<\/em>. Applied Mathematics &amp; Optimization. <a rel=\"noreferrer noopener\" href=\"https:\/\/link.springer.com\/article\/10.1007\/s00245-022-09836-5\" data-type=\"URL\" data-id=\"https:\/\/link.springer.com\/article\/10.1007\/s00245-022-09836-5\" target=\"_blank\">Lin<\/a><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s00245-022-09836-5\" data-type=\"URL\" data-id=\"https:\/\/link.springer.com\/article\/10.1007\/s00245-022-09836-5\" target=\"_blank\" rel=\"noreferrer noopener\">k<\/a><a rel=\"noreferrer noopener\" href=\"https:\/\/link.springer.com\/article\/10.1007\/s00245-022-09836-5\" data-type=\"URL\" data-id=\"https:\/\/link.springer.com\/article\/10.1007\/s00245-022-09836-5\" target=\"_blank\"><\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, <em>upU-Net Approaches for Background Emission Removal in Fluorescence Microscopy<\/em>, Journal of Imaging. <a rel=\"noreferrer noopener\" href=\"https:\/\/www.mdpi.com\/2313-433X\/8\/5\/142\" data-type=\"URL\" data-id=\"https:\/\/www.mdpi.com\/2313-433X\/8\/5\/142\" target=\"_blank\">Link<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>G. Aletti, A. Benfenati, G. Naldi. <em>A New Nonlocal Nonlinear Diffusion Equation: the one-Dimensional Case<\/em>, Bulletin of the Australian Mathematical Society. <a rel=\"noreferrer noopener\" href=\"https:\/\/www.cambridge.org\/core\/journals\/bulletin-of-the-australian-mathematical-society\/article\/new-nonlocal-nonlinear-diffusion-equation-the-onedimensional-case\/1AA2C74AADE6A80D057B4866B4F6A9F5\" target=\"_blank\">Link<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>G. Aletti, A. Benfenati, G. Naldi.  <em>A Semi-supervised Multilabel Segmentation Method for Hyperspectral<\/em> <em>images<\/em>. Journal of Imaging. <a rel=\"noreferrer noopener\" href=\"https:\/\/www.mdpi.com\/2313-433X\/7\/12\/267\" data-type=\"URL\" data-id=\"https:\/\/www.mdpi.com\/2313-433X\/7\/12\/267\" target=\"_blank\">Article<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>G. Aletti, A. Benfenati, G. Naldi. <em>Graph, spectra, control and epidemics: an example with a SEIR model<\/em>, Mathematics. <a href=\"https:\/\/www.mdpi.com\/2227-7390\/9\/22\/2987\" data-type=\"URL\" data-id=\"https:\/\/www.mdpi.com\/2227-7390\/9\/22\/2987\">Article<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>G. Aletti, A. Benfenati, G. Naldi. <em>A Semiautomatic Multi-Label Color Image Segmentation Coupling Dirichlet Problem and Colour Distances<\/em>, Journal of Imaging. <a href=\"https:\/\/doi.org\/10.3390\/jimaging7100208\" data-type=\"URL\" data-id=\"https:\/\/doi.org\/10.3390\/jimaging7100208\">Article<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, E. Chouzenoux, and J.-C. Pesquet. <em>Proximal Approaches for Matrix Optimization Problems: Application to Robust Precision Matrix Estimation<\/em>, Signal Processing. <a href=\"https:\/\/doi.org\/10.1016\/j.sigpro.2019.107417\">Article<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, F. Bonacci, T. Bourouina, H. Talbot. <em>Efficient segmentation and positioning of 3D fluorescent spherical beads in confocal microscopy<\/em>, Journal of Mathematical Imaging and Vision. <a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10851-020-00994-1\">Article<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, E. Chouzenoux, L. Duval, J.-C. Pesquet, A. Pirayre. <em>A review on graph optimization and algorithmic frameworks<\/em>. HAL Archives, 2018. <a href=\"https:\/\/hal.archives-ouvertes.fr\/hal-01901499\/\">pdf<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>S. Bonettini, A. Benfenati, and V. Ruggiero. <em>Scaling techniques for epsilon-subgradient methods<\/em>. SIAM Journal on Optimization, 26(3):1741-1772, 2016.<a href=\"https:\/\/doi.org\/10.1137\/14097642X\"> Article<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, A. La Camera, and M. Carbillet. <em>Deconvolution of post-adaptive optics images of faint circumstellar environments by means of the inexact Bregman procedure<\/em>. Astronomy &amp; Astrophysics, 586:A16, 2016. <a href=\"https:\/\/www.aanda.org\/articles\/aa\/pdf\/2016\/02\/aa26960-15.pdf&quot;&quot;\">Article<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>L. Zanni, A. Benfenati, M. Bertero, and V. Ruggiero. <em>Numerical methods for parameter estimation in Poisson data inversion<\/em>. Journal of Mathematical Imaging and Vision, 52(3):397-413, 2015. <a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10851-014-0553-9\">Article<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati and V. Ruggiero. <em>Inexact Bregman iteration for deconvolution of superimposed extended and point sources<\/em>. Communications in Nonlinear Science and Numerical Simulation, 21(1):210-224, 2015.<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1007570414003086\"> Article<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati and V. Coscia. <em>Modeling opinion formation in the kinetic theory of active particles I: spontaneous trend<\/em>. Annali dell&#8217;Universit\u00e0 di Ferrara, 60(1):35-53, 2014. <a href=\"https:\/\/link.springer.com\/article\/10.1007\/s11565-014-0207-2\">Article<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A.Benfenati and V.Ruggiero.<em>Inexact Bregman iteration with an application to Poisson data reconstruction<\/em>. Inverse Problems, 29(6):065016, 2013. <a href=\"http:\/\/iopscience.iop.org\/article\/10.1088\/0266-5611\/29\/6\/065016\">Article<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati and V. Coscia. <em>Nonlinear microscale interactions in the kinetic theory of active particles<\/em>. Applied Mathematics Letters, 26(10):979-983, 2013. <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0893965913001213\">Article<\/a><\/li><\/ul>\n\n\n\n<p style=\"font-size:30px\"><a name=\"proceedings\">Proceedings<\/a><a href=\"https:\/\/sites.unimi.it\/a_benfenati\/publications\/\">\u2191<\/a><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, A. Catozzi, G. Franchini and F. Porta, Piece-wise Constant Image Segmentation with&nbsp;a&nbsp;Deep Image Prior Approach, Scale Space and Variational Methods in Computer Vision. SSVM 2023. Lecture Notes in Computer Science, vol 14009. <a rel=\"noreferrer noopener\" href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-31975-4_27\" data-type=\"URL\" data-id=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-31975-4_27\" target=\"_blank\">Link<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, P. Causin,, M.G. Lupieri and G. Naldi, <em>Regularization Techniques for Inverse Problem in DOT Applications<\/em>, Journal of Physics: Conference Series, 1476, 2020, 012007 <a href=\"https:\/\/iopscience.iop.org\/article\/10.1088\/1742-6596\/1476\/1\/012007\/pdf\">pdf<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>\u00c9. Puybareau, E. Carlinet, A. Benfenati and H. Talbot, <em>Spherical Fluorescent Particle Segmentation and Tracking in 3D Confocal Microscopy<\/em>, Mathematical Morphology and Its Applications to Signal and Image Processing (ISMM) 2019. Lecture Notes in Computer Science, vol 11564. Springer, Cham. <a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-030-20867-7_40\">pdf<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, E. Chouzenoux, and J.-C. Pesquet. <em>A Nonconvex Variational Approach for Robust Graphical Lasso.<\/em>. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018. <a href=\"https:\/\/ieeexplore.ieee.org\/document\/8462421\">pdf<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A. Benfenati, E. Chouzenoux, and J.-C. Pesquet. <em>A proximal approach for solving matrix optimization problems involving a Bregman divergence<\/em>. In Proceedings of the International Biomedical and Astronomical Signal Processing Frontiers workshop (BASP), 2017. <a href=\"http:\/\/www.baspfrontiers.org\/archive\/2017\/proceedings.php.html\">pdf<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Benfenati, V Ruggiero, <em>Image regularization for Poisson data <\/em>, Journal of Physics: Conference Series, 657, 2015, 012011, <a href=\"http:\/\/iopscience.iop.org\/article\/10.1088\/1742-6596\/657\/1\/012011\/pdf\">pdf<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>S. Bonettini, A. Benfenati, and V. Ruggiero. <em>Primal-dual first order methods for total variation image restoration in presence of Poisson noise<\/em>. In 2014 IEEE International Conference on Image Processing (ICIP), pages 4156-4160, 2014. <a href=\"http:\/\/ieeexplore.ieee.org\/abstract\/document\/7025844\/\">link<\/a><\/li><\/ul>\n\n\n\n<p style=\"font-size:30px\"><a name=\"conf\">Attended Conferences and Workshops<\/a> <a href=\"https:\/\/sites.unimi.it\/a_benfenati\/publications\/\">\u2191<\/a><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>3-5 December 2025, <em>Unveiling Transformer Perception by Exploring Input Manifolds<\/em>, Eurips25, Copenaghen, Denmark<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>29 September &#8211; 1 October 2025, PnP Splitting Approaches for Poisson Image Restoration, EUCCO25, Klagenfurt, Austria<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>28 July &#8211; 1 August 2025, <em>Latent Space Techniques for Diffuse Optical Tomography Inverse Problems<\/em> and <em>Regularization via Latent Diffusion Models for Image Restoration<\/em>, <a rel=\"noreferrer noopener\" href=\"https:\/\/eventos.fgv.br\/aip2025\" data-type=\"URL\" data-id=\"https:\/\/eventos.fgv.br\/aip2025\" target=\"_blank\">Applied Inverse Problems 2025<\/a>, Rio de Janeiro, Brazil<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>29 June &#8211; 2 July 2025, <em>Neural Blind Deconvolution for Poisson Data<\/em>, <a href=\"https:\/\/europt2025.org\" data-type=\"URL\" data-id=\"https:\/\/europt2025.org\" target=\"_blank\" rel=\"noreferrer noopener\">EUROPT 2025<\/a>, Southampton (UK)<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>4-6 June 2025, <em>Deep Image Segmentation: Variational Approaches Interact WithDeep Image Prior<\/em>, Math 2 Production, Valencia, Spain<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>12-13 March 2025, <em>Deep Learning Techniques for Imaging Problems: Deep Image Prior and AutoEncoders for blind deconvolution, segmentation and tomography reconstruction<\/em>, <a href=\"https:\/\/sites.google.com\/view\/advancednumericalmethods4mldl\/follow-up-workshop?authuser=0\" data-type=\"URL\" data-id=\"https:\/\/sites.google.com\/view\/advancednumericalmethods4mldl\/follow-up-workshop?authuser=0\" target=\"_blank\" rel=\"noreferrer noopener\">Two-days Follow on Advanced Numerical Methods for Machine &amp; Deep Learning<\/a>, Ferrara, Italy<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>27-31 May 2024, <em>Poisson Tailored Neural Blind Deconvolution for Biomedical Imaging<\/em>, <a rel=\"noreferrer noopener\" href=\"https:\/\/www.siam.org\/conferences\/cm\/conference\/is24\" data-type=\"URL\" data-id=\"https:\/\/www.siam.org\/conferences\/cm\/conference\/is24\" target=\"_blank\">SIAM Conference on Imaging Science 24<\/a>,  Atlanta (GA), USA.<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>4-8 September 2023, <em>Investigating the Human Body by Light: Neural Networks for Data-Driven and Physics-Driven Approches<\/em>, <a href=\"http:\/\/www.aip2023.com\" data-type=\"URL\" data-id=\"www.aip2023.com\" target=\"_blank\" rel=\"noreferrer noopener\">Applied Inverse Problem conference 2023,<\/a> Gottingen, Germany<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>28 August &#8211; 1 September 2023, <em>Imaging Problems in Deep Learning Framework<\/em>, <a href=\"https:\/\/smile.lakecomoschool.org\/\">SMILE Sustainable Medical Imaging with Learning and Regularization<\/a> doctoral school, Como, Italy<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>21-23 May  2023, <em>Piece-wise Constant Image Segmentation with&nbsp;a&nbsp;Deep Image Prior Approach<\/em>, 9th International Conference Scale Space and Variational Methods in Computer Vision, Santa Margherita di Pula, Italy<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>29-30 September 2022, <em>LearnedSVD Approach for Diffuse Optical Tomography<\/em>: <em>Classical and Deep Learning Techniques<\/em>, GIMC SIMAI YOUNG 2022, Pavia, Italy<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>21-25 March 2022,  <em>Regularization Approaches for DOT Inverse Problems: Classical and Deep Learning Techniques<\/em>, Virtual conference, <a rel=\"noreferrer noopener\" href=\"https:\/\/www.siam.org\/conferences\/cm\/conference\/is22\" data-type=\"URL\" data-id=\"https:\/\/www.siam.org\/conferences\/cm\/conference\/is22\" target=\"_blank\">SIAM Conference on Imaging Science<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>22-23 November 2021, Invited Speaker: <em>Position Estimation of 3D Spherical Beads in Confocal Microscopy via Poisson Denoising using Bregman Iterations<\/em>, <a href=\"https:\/\/sites.google.com\/view\/workshopvamos\" data-type=\"URL\" data-id=\"https:\/\/sites.google.com\/view\/workshopvamos\">Advanced optimization methods for inverse problems and applications to image microscopy<\/a>, Firenze<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>11-13 October 2021, <em>A Local-Global Graph Approach for Coloured Image Segmentation<\/em>, <a href=\"https:\/\/events.unibo.it\/primo-workshop-2021\/schedule\" data-type=\"URL\" data-id=\"https:\/\/events.unibo.it\/primo-workshop-2021\/schedule\">PRIMO Workshop<\/a>, Bologna<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>30 August &#8211; 3 Settember 2021, Organizer of the Mini Simposium  <em>Inverse Problems: New Frontiers in Bio\u2013Medical Imaging<\/em>, SIMAI2020-2021, Parma, Italy<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>1-5 March 2020, <em>Deep\u2013Learning Based Regularization for DOT Inverse Problems<\/em>, SIAM Conference on Computational Science and Engineering (CSE21), Virtual Conference<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>18 June 2019, <em>Proximal Approaches for Matrix Estimation Problems<\/em> (talk), NUMTA 2019, Le Castella, Italy;<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>24 May 2019, <em>Regularization Techniques for Inverse Problem in DOT Applications<\/em>, NCMIP 2019, Cachan, France<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>10 May 2019, <em>On a scaled epsilon-subgradient methods<\/em> (talk), TiciNUM 2019, Pavia, Italy;<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>15-20 April 2018, <em>A Nonconvex Variational Approach for Robust Graphical LASSO<\/em> (talk), IEEE ICASSP 2018, Calgary, Alberta, Canada;<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>22 May-25 May 2017, <em>On a Scaled epsilon-subgradient Method with Adaptive Stepsize Rule<\/em> (talk), SIAM Conference on Optimization 2017, Vancouver, British Columbia, Canada;<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>29 January &#8211; 3 February 2017, <em>A Proximal Approach for Solving matrix Optimization Problems Involving a Bregman Divergence<\/em> (poster), International BASP Frontiers workshop 2017, Heriot-Watt University, United Kingdom<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>22 September 2016, <em>Scaling techniques for epsilon-subgradient methods<\/em> (poster), Optimization Techniques for Inverse Problems III, Universit\u00e0 degli Studi di Modena e Reggio Emilia, Modena, Italy<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>15 September 2016, <em>A scaled epsilon-subgradient method<\/em> (talk), SIMAI 2016, Politecnico di Milano, Milano, Italy<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>29 May 2015, <em>Image regularization for Poisson data<\/em> (poster), 5th International Workshop on New Computational Methods for Inverse Problems, Institut Farman, Ecole Normale Sup erieure de Cachan, Cachan, France<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>8-12 September 2014, <em>Inexact Bregman Regularization for Poisson Data<\/em> (talk), Optimization and dynamical processes in statistical learning and inverse problems, DIMA-Universit\u00e0 degli studi di Genova, Genova, Italy<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>7-10 July 2014, <em>Inexact Bregman Regularization for Astronomical Images Corrupted by Poisson Noise<\/em> (talk), SIMAI 2014, SIMAI<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>26 June 2013 &#8211; 28 June 2013, <em>Inexact Bregman Iteration with applications to Poisson data reconstruction<\/em> (talk), 11th EUROPT Workshop on Advances in Continuous Optimization, Universit\u00e0 degli Studi di Firenze, Firenze, Italy<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>17 June 2013 &#8211; 23 June 2013, <em>Image Restoration from Poisson Data by an Inexact Bregman Iteration Scheme<\/em> (talk), Numerical Computations: Theory and Algorithms, Universit\u00e0 della Calabria, with the collaboration of Lobachevsky State University, Italy<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>31 January 2013 &#8211; 1 February 2013, <em>Su un modello di formazione di opinioni: Considerazioni generali e primi risultati<\/em> (talk), GA-MeMoMa- COMPLEX-SIMAI &#8211; Incontro plenario, Politecnico di Torino, Torino, Italy<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>20 September 2012 &#8211; 21 September 2012, <em>Image Restoration from Poisson data by Bregman Iteration<\/em> (poster), Optimization Techniques for Inverse Problems II, Universit\u00e0 degli studi di Modena e Reggio Emilia, Modena, Italy<\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>23 April 2012 &#8211; 27 April 2012, <em>Image restoration of Poisson data with iterative Bregman regularization procedure<\/em> (talk), Computational Inverse Problems, Erwin Schroedinger Institut, Vienna, Austria<\/li><\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Submission Papers Proceedings Conferences New Submissions\u2191 P. Cascarano, L. Stacchio,&nbsp;A. Sebastiani,&nbsp;A. Benfenati,&nbsp;U. S. Kamilov,&nbsp;G. Marfia, RELD: Regularization by Latent Diffusion&hellip;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-27","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.unimi.it\/a_benfenati\/wp-json\/wp\/v2\/pages\/27","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.unimi.it\/a_benfenati\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.unimi.it\/a_benfenati\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.unimi.it\/a_benfenati\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.unimi.it\/a_benfenati\/wp-json\/wp\/v2\/comments?post=27"}],"version-history":[{"count":98,"href":"https:\/\/sites.unimi.it\/a_benfenati\/wp-json\/wp\/v2\/pages\/27\/revisions"}],"predecessor-version":[{"id":398,"href":"https:\/\/sites.unimi.it\/a_benfenati\/wp-json\/wp\/v2\/pages\/27\/revisions\/398"}],"wp:attachment":[{"href":"https:\/\/sites.unimi.it\/a_benfenati\/wp-json\/wp\/v2\/media?parent=27"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}