Spatially Varying Regularisation
Mathematical analysis of spatially adaptive total-variation (TV) and TV-type regularisers for imaging inverse problems and reconstruction tasks.
I am interested in the theory of variational models for imaging inverse problems, with a particular focus on the functional-analytic properties of spatially varying regularisation methods. In particular, my work lies at the intersection of functional analysis, geometric measure theory, and functions of bounded variation (BV), where I study the analytical properties of reconstruction models and their interaction with modern learning-based techniques.
Mathematical analysis of spatially adaptive total-variation (TV) and TV-type regularisers for imaging inverse problems and reconstruction tasks.
Functional-analytic and measure-theoretic tools for studying existence, stability, and analytical properties of reconstruction models.
Variational optimisation methods, unrolled iterative schemes, and combined learning-based approaches to image reconstruction.
Selected publications and preprints.
Mark Wrobel, Michele Pascale, Tina Yao, Ruaraidh Campbell, Elena Milano, Michael Quail, Jennifer Steeden, Vivek Muthurangu
Michele Pascale
Olivier Jaubert, Michele Pascale, Javier Montalt-Tordera, Julius Akesson, Ruta Virsinskaite, Daniel Knight, Simon Arridge, Jennifer Steeden, Vivek Muthurangu
Selected research talks, presentations and posters.