Trustworthy Image Super-Resolution via Generative Pseudoinverse
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908369269293056 |
|---|---|
| author | Floros, Andreas Moosavi-Dezfooli, Seyed-Mohsen Dragotti, Pier Luigi |
| author_facet | Floros, Andreas Moosavi-Dezfooli, Seyed-Mohsen Dragotti, Pier Luigi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12375 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trustworthy Image Super-Resolution via Generative Pseudoinverse Floros, Andreas Moosavi-Dezfooli, Seyed-Mohsen Dragotti, Pier Luigi Image and Video Processing Machine Learning 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. |
| title | Trustworthy Image Super-Resolution via Generative Pseudoinverse |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2505.12375 |