mlsanityData to 14 Oct 2026

Trustworthy Image Super-Resolution via Generative Pseudoinverse

arXiv 2505.12375 ↗First posted 18 May 2025eess.IV cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 16 references that are in the map. First posted on arXiv 18 May 2025.

Builds on

14 of 16 references.

References · 14

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  3. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  4. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  5. 2016Density estimation using Real NVPcited by 2,042
  6. 2021Image Super-Resolution via Iterative Refinementcited by 1,511
  7. 2020PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelscited by 358
  8. 2018Analyzing Inverse Problems with Invertible Neural Networkscited by 242
  9. 2023Implicit Diffusion Models for Continuous Super-Resolutioncited by 218
  10. 2017FSRNet: End-to-End Learning Face Super-Resolution with Facial Priorscited by 170
  11. 2021Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescalingcited by 73
  12. 2023Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolutioncited by 19
2 more
  1. 2022Invertible Rescaling Network and Its Extensionscited by 15
  2. 2024Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Modelscited by 12

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025On the Anisotropy of Score-Based Generative Modelscited by 1