Solving Inverse Problems by Joint Posterior Maximization with Autoencoding Prior

1 code implementation • 2 Mar 2021 • Mario González, Andrés Almansa, Pauline Tan

Whereas previous MAP-based approaches to this problem lead to highly non-convex optimization algorithms, our approach computes the joint (space-latent) MAP that naturally leads to alternate optimization algorithms and to the use of a stochastic encoder to accelerate computations.

Denoising

Solving Inverse Problems by Joint Posterior Maximization with a VAE Prior

1 code implementation • 14 Nov 2019 • Mario González, Andrés Almansa, Mauricio Delbracio, Pablo Musé, Pauline Tan

In this paper we address the problem of solving ill-posed inverse problems in imaging where the prior is a neural generative model.