3 papers
math.OC2026
A Modular Approach to Stochastic Optimisation for Inverse Problems Using the Core Imaging Library
Evangelos Papoutsellis, Margaret A. G. Duff, Jakob S. Jørgensen +5
The Core Imaging Library (CIL) is an open-source versatile Python framework for solving inverse problems with special emphasis on imaging applications such as computed tomography (…
math.OC2026
Split, Skip and Play: Variance-Reduced ProxSkip for Tomography Reconstruction is Extremely Fast
Evangelos Papoutsellis, Zeljko Kereta, Kostas Papafitsoros
Many modern iterative solvers for large-scale tomographic reconstruction incur two major computational costs per iteration: expensive forward/adjoint projections to update the data…
math.NA2024
Why do we regularise in every iteration for imaging inverse problems?
Evangelos Papoutsellis, Zeljko Kereta, Kostas Papafitsoros
Regularisation is commonly used in iterative methods for solving imaging inverse problems. Many algorithms involve the evaluation of the proximal operator of the regularisation ter…