Steerable conditional diffusion for out-of-distribution adaptation in imaging inverse problems
Denoising diffusion models have emerged as the go-to framework for solving inverse
problems in imaging. A critical concern regarding these models is their performance on out …
problems in imaging. A critical concern regarding these models is their performance on out …
Score-based generative models for PET image reconstruction
Score-based generative models have demonstrated highly promising results for medical
image reconstruction tasks in magnetic resonance imaging or computed tomography …
image reconstruction tasks in magnetic resonance imaging or computed tomography …
AmbientFlow: Invertible generative models from incomplete, noisy measurements
VA Kelkar, R Deshpande, A Banerjee… - arxiv preprint arxiv …, 2023 - arxiv.org
Generative models have gained popularity for their potential applications in imaging
science, such as image reconstruction, posterior sampling and data sharing. Flow-based …
science, such as image reconstruction, posterior sampling and data sharing. Flow-based …
Missing wedge completion via unsupervised learning with coordinate networks
Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling
detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its …
detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its …
Conditional score-based diffusion models for solving inverse problems in mechanics
We propose a framework to perform Bayesian inference using conditional score-based
diffusion models to solve a class of inverse problems in mechanics involving the inference of …
diffusion models to solve a class of inverse problems in mechanics involving the inference of …
Benchmarking learned algorithms for computed tomography image reconstruction tasks
Computed tomography (CT) is a widely used non-invasive diagnostic method in various
fields, and recent advances in deep learning have led to significant progress in CT image …
fields, and recent advances in deep learning have led to significant progress in CT image …
Provably convergent data-driven convex-nonconvex regularization
An emerging new paradigm for solving inverse problems is via the use of deep learning to
learn a regularizer from data. This leads to high-quality results, but often at the cost of …
learn a regularizer from data. This leads to high-quality results, but often at the cost of …
Conditional score-based diffusion models for solving inverse elasticity problems
We propose a framework to perform Bayesian inference using conditional score-based
diffusion models to solve a class of inverse problems in mechanics involving the inference of …
diffusion models to solve a class of inverse problems in mechanics involving the inference of …
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation
Variational regularisation is the primary method for solving inverse problems, and recently
there has been considerable work leveraging deeply learned regularisation for enhanced …
there has been considerable work leveraging deeply learned regularisation for enhanced …
Conditional score-based generative models for solving physics-based inverse problems
We propose to sample from high-dimensional posterior distributions arising in physics-
based inverse problems using conditional score-based generative models. The proposed …
based inverse problems using conditional score-based generative models. The proposed …