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.
@article{arxiv.2505.12375,
title = {Trustworthy Image Super-Resolution via Generative Pseudoinverse},
author = {Andreas Floros and Seyed-Mohsen Moosavi-Dezfooli and Pier Luigi Dragotti},
journal= {arXiv preprint arXiv:2505.12375},
year = {2025}
}