Papers › Trustworthy Image Super-Resolution via Generative Pseudoinverse
Trustworthy Image Super-Resolution via Generative Pseudoinverse
Andreas Floros, Seyed-Mohsen Moosavi-Dezfooli, Pier Luigi Dragotti
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Super-Resolution | CelebA-HQ 128x128 | TSRGP | Consistency | 0.31 | #1 of 4 | Archive leaderboard | report |
| Image Super-Resolution | CelebA-HQ 128x128 | TSRGP | PSNR | 24.09 | #1 of 4 | Archive leaderboard | report |
| Image Super-Resolution | CelebA-HQ 128x128 | TSRGP | SSIM | 0.71 | #1 of 4 | Archive leaderboard | report |
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Methods
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