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.
@article{arxiv.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}
}