Poster
in
Workshop: Deep Generative Model in Machine Learning: Theory, Principle and Efficacy

Trustworthy Image Super-Resolution via Generative Pseudoinverse

Andreas Floros ⋅ Seyed-Mohsen Moosavi-Dezfooli ⋅ Pier Dragotti

Keywords: normalizing flows Structured data modeling super-resolution diffusion models

Project Page [ OpenReview]

Abstract

We consider the problem of trustworthy image restoration, taking the form of a constrained optimization over the prior density. To this end, we develop generative models for the task of image super-resolution that respect the degradation process and that can be made asymptotically consistent with the low-resolution measurements, outperforming existing methods by a large margin in that respect. Code will be released upon acceptance.

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