Tootfinder

A novel method and dataset for depth-guided image deblurring from smartphone Lidar
Antonio Montanaro, Diego Valsesia
https://arxiv.org/abs/2509.09241 https://

A novel method and dataset for depth-guided image deblurring from smartphone Lidar
Modern smartphones are equipped with Lidar sensors providing depth-sensing capabilities. Recent works have shown that this complementary sensor allows to improve various tasks in image processing, including deblurring. However, there is a current lack of datasets with realistic blurred images and paired mobile Lidar depth maps to further study the topic. At the same time, there is also a lack of blind zero-shot methods that can deblur a real image using the depth guidance without requiring exte…

Probing the formation of megaparsec-scale giant radio galaxies II. Continuum & polarization behavior from MHD simulations
Gourab Giri, Christian Fendt, Joydeep Bagchi, Kshitij Thorat, D. J. Saikia, Roger P. Deane, Jacinta Delhaize
https://arxiv.org/abs/2510.03037

Probing the formation of megaparsec-scale giant radio galaxies II. Continuum & polarization behavior from MHD simulations
The persistence of radiative signatures in giant radio galaxies remains a frontier topic of research, with contemporary telescopes revealing intricate features that require investigation. This study aims to examine the emission characteristics of simulated GRGs, and correlate them with their underlying 3D dynamical properties. Sky-projected continuum and polarization maps at 1 GHz were computed from five 3D-RMHD simulations by integrating the synthesized emissivity data along the line of sight,…

Unsupervised Ground Metric Learning
Janis Auffenberg, Jonas Bresch, Oleh Melnyk, Gabriele Steidl
https://arxiv.org/abs/2507.13094 https://

Unsupervised Ground Metric Learning
Data classification without access to labeled samples remains a challenging problem. It usually depends on an appropriately chosen distance between features, a topic addressed in metric learning. Recently, Huizing, Cantini and Peyré proposed to simultaneously learn optimal transport (OT) cost matrices between samples and features of the dataset. This leads to the task of finding positive eigenvectors of a certain nonlinear function that maps cost matrices to OT distances. Having this basic ide…