mlsanityData to 7 Oct 2026

Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems

arXiv 2505.09528 ↗First posted 14 May 2025cs.CVcited by 2

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Abstract (arXiv)

In imaging inverse problems, we would like to know how close the recovered image is to the true image in terms of full-reference image quality (FRIQ) metrics like PSNR, SSIM, LPIPS, etc. This is especially important in safety-critical applications like medical imaging, where knowing that, say, the SSIM was poor could potentially avoid a costly misdiagnosis. But since we don't know the true image, computing FRIQ is non-trivial. In this work, we combine conformal prediction with approximate posterior sampling to construct bounds on FRIQ that are guaranteed to hold up to a user-specified error probability. We demonstrate our approach on image denoising and accelerated magnetic resonance imaging (MRI) problems. Code is available at https://github.com/jwen307/quality_uq.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 48 of its 72 references that are in the map. First posted on arXiv 14 May 2025.

Builds on

48 of 72 references.

References · 48

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36 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026Flow-Based Conformal Predictive Distributionscited by 2
  2. 2025Minimax Multi-Target Conformal Prediction with Applications to Imaging Inverse Problemscited by 0