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

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 atthis https URL.

@article{wen2025_2505.09528,
  title={ Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems },
  author={ Jeffrey Wen and Rizwan Ahmad and Philip Schniter },
  journal={arXiv preprint arXiv:2505.09528},
  year={ 2025 }
}