Improving diffusion models for inverse problems using manifold constraints
Recently, diffusion models have been used to solve various inverse problems in an
unsupervised manner with appropriate modifications to the sampling process. However, the …
unsupervised manner with appropriate modifications to the sampling process. However, the …
Bayesian imaging using plug & play priors: when langevin meets tweedie
Since the seminal work of Venkatakrishnan, Bouman, and Wohlberg [Proceedings of the
Global Conference on Signal and Information Processing, IEEE, 2013, pp. 945--948] in …
Global Conference on Signal and Information Processing, IEEE, 2013, pp. 945--948] in …
Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolution
Using diffusion models to solve inverse problems is a growing field of research. Current
methods assume the degradation to be known and provide impressive results in terms of …
methods assume the degradation to be known and provide impressive results in terms of …
Image reconstruction algorithms in radio interferometry: From handcrafted to learned regularization denoisers
We introduce a new class of iterative image reconstruction algorithms for radio
interferometry, at the interface of convex optimization and deep learning, inspired by plug …
interferometry, at the interface of convex optimization and deep learning, inspired by plug …
Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging
Next-generation radio interferometers like the Square Kilometer Array have the potential to
unlock scientific discoveries thanks to their unprecedented angular resolution and …
unlock scientific discoveries thanks to their unprecedented angular resolution and …
A proximal Markov chain Monte Carlo method for Bayesian inference in imaging inverse problems: When Langevin meets Moreau
Modern imaging methods rely strongly on Bayesian inference techniques to solve
challenging imaging problems. Currently, the predominant Bayesian computational …
challenging imaging problems. Currently, the predominant Bayesian computational …
Particle algorithms for maximum likelihood training of latent variable models
Neal and Hinton (1998) recast maximum likelihood estimation of any given latent variable
model as the minimization of a free energy functional F, and the EM algorithm as coordinate …
model as the minimization of a free energy functional F, and the EM algorithm as coordinate …
On maximum a posteriori estimation with plug & play priors and stochastic gradient descent
Bayesian methods to solve imaging inverse problems usually combine an explicit data
likelihood function with a prior distribution that explicitly models expected properties of the …
likelihood function with a prior distribution that explicitly models expected properties of the …
Accelerating proximal Markov chain Monte Carlo by using an explicit stabilized method
We present a highly efficient proximal Markov chain Monte Carlo methodology to perform
Bayesian computation in imaging problems. Similarly to previous proximal Monte Carlo …
Bayesian computation in imaging problems. Similarly to previous proximal Monte Carlo …
Computationally efficient sampling methods for sparsity promoting hierarchical Bayesian models
D Calvetti, E Somersalo - SIAM/ASA Journal on Uncertainty Quantification, 2024 - SIAM
Bayesian hierarchical models have been demonstrated to provide efficient algorithms for
finding sparse solutions to ill-posed inverse problems. The models comprise typically a …
finding sparse solutions to ill-posed inverse problems. The models comprise typically a …