15 citations · 32 across the 24 of their papers we have counts for
27 papers
One for All, All for One: Coordinated Multi-Agent Diffusion Steering via Stochastic Optimal Control
Riccardo Barbano, Vincent Pauline, Runchang Li +6
Deep generative models often produce structured outputs composed of interacting components. Modelling these outputs with a single model requires learning both the component distrib…
Deep Image Prior for Computed Tomography Reconstruction
Simon Arridge, Riccardo Barbano, Alexander Denker +1
We present a comprehensive overview of the Deep Image Prior (DIP) framework and its applications to image reconstruction in computed tomography. Unlike conventional deep learning m…
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…
Trajectory Stitching for Solving Inverse Problems with Flow-Based Models
Alexander Denker, Moshe Eliasof, Zeljko Kereta +1
Flow-based generative models have emerged as powerful priors for solving inverse problems. One option is to directly optimize the initial latent code (noise), such that the flow ou…
CMAD: Cooperative Multi-Agent Diffusion via Stochastic Optimal Control
Riccardo Barbano, Alexander Denker, Zeljko Kereta +2
Continuous-time generative models have achieved remarkable success in image restoration and synthesis. However, controlling the composition of multiple pre-trained models remains a…
Stochastic Gradient Descent for Nonlinear Inverse Problems in Banach Spaces
Bangti Jin, Zeljko Kereta, Yuxin Xia
Stochastic gradient descent (SGD) and its variants are widely used and highly effective optimization methods in machine learning, especially for neural network training. By using a…
Graph Neural Regularizers for PDE Inverse Problems
William Lauga, James Rowbottom, Alexander Denker +3
We present a framework for solving a broad class of ill-posed inverse problems governed by partial differential equations (PDEs), where the target coefficients of the forward opera…
Learning Binary Sampling Patterns for Single-Pixel Imaging using Bilevel Optimisation
Serban Cristian Tudosie, Alexander Denker, Zeljko Kereta +1
Single-Pixel Imaging (SPI) enables the reconstruction of objects using a single detector through sequential illuminations with structured light patterns. The choice of illumination…
Fast PET Reconstruction with Variance Reduction and Prior-Aware Preconditioning
Matthias J. Ehrhardt, Zeljko Kereta, Georg Schramm
We investigate subset-based optimization methods for positron emission tomography (PET) image reconstruction incorporating a regularizing prior. PET reconstruction methods that use…
Plug-and-Play Half-Quadratic Splitting for Ptychography
Alexander Denker, Johannes Hertrich, Zeljko Kereta +3
Ptychography is a coherent diffraction imaging method that uses phase retrieval techniques to reconstruct complex-valued images. It achieves this by sequentially illuminating overl…
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…
Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data
Alexander Denker, Zeljko Kereta, Imraj Singh +4
Electrical impedance tomography (EIT) plays a crucial role in non-invasive imaging, with both medical and industrial applications. In this paper, we present three data-driven recon…
Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction
Evangelos Papoutsellis, Casper da Costa-Luis, Daniel Deidda +10
We introduce a stochastic framework into the open--source Core Imaging Library (CIL) which enables easy development of stochastic algorithms. Five such algorithms from the literatu…
A Guide to Stochastic Optimisation for Large-Scale Inverse Problems
Matthias J. Ehrhardt, Zeljko Kereta, Jingwei Liang +1
Stochastic optimisation algorithms are the de facto standard for machine learning with large amounts of data. Handling only a subset of available data in each optimisation step dra…
Convergence Properties of Score-Based Models for Linear Inverse Problems Using Graduated Optimisation
Pascal Fernsel, Željko Kereta, Alexander Denker
The incorporation of generative models as regularisers within variational formulations for inverse problems has proven effective across numerous image reconstruction tasks. However…
Stochastic gradient descent for linear inverse problems in variable exponent Lebesgue spaces
Marta Lazzaretti, Zeljko Kereta, Luca Calatroni +1
We consider a stochastic gradient descent (SGD) algorithm for solving linear inverse problems (e.g., CT image reconstruction) in the Banach space framework of variable exponent Leb…
On the Convergence of Stochastic Gradient Descent for Linear Inverse Problems in Banach Spaces
Z. Kereta, B. Jin
In this work we consider stochastic gradient descent (SGD) for solving linear inverse problems in Banach spaces. SGD and its variants have been established as one of the most succe…
StreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm
Andreas Oslandsbotn, Zeljko Kereta, Valeriya Naumova +2
Kernel ridge regression (KRR) is a popular scheme for non-linear non-parametric learning. However, existing implementations of KRR require that all the data is stored in the main m…
Unsupervised Knowledge-Transfer for Learned Image Reconstruction
Riccardo Barbano, Zeljko Kereta, Andreas Hauptmann +2
Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities. These approaches usually require a large amount o…
Stochastic EM methods with Variance Reduction for Penalised PET Reconstructions
Zeljko Kereta, Robert Twyman, Simon Arridge +2
Expectation-maximization (EM) is a popular and well-established method for image reconstruction in positron emission tomography (PET) but it often suffers from slow convergence. Or…
Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction
Riccardo Barbano, Željko Kereta, Chen Zhang +3
Image reconstruction methods based on deep neural networks have shown outstanding performance, equalling or exceeding the state-of-the-art results of conventional approaches, but o…
Construction and Monte Carlo estimation of wavelet frames generated by a reproducing kernel
Ernesto De Vito, Zeljko Kereta, Valeriya Naumova +2
We introduce a construction of multiscale tight frames on general domains. The frame elements are obtained by spectral filtering of the integral operator associated with a reproduc…
Estimating covariance and precision matrices along subspaces
Zeljko Kereta, Timo Klock
We study the accuracy of estimating the covariance and the precision matrix of a -variate sub-Gaussian distribution along a prescribed subspace or direction using the finite sam…
Computational approaches to non-convex, sparsity-inducing multi-penalty regularization
Zeljko Kereta, Johannes Maly, Valeriya Naumova
In this work we consider numerical efficiency and convergence rates for solvers of non-convex multi-penalty formulations when reconstructing sparse signals from noisy linear measur…