Work

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

Year: 2025

Type: article

Abstract: 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.

Source: PubMed

DOI: 10.48550/arxiv.2505.09528

Authors: Wen, Jeffrey, Ahmad, Rizwan, Schniter, Philip

Institution: The Ohio State University

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering

Award: A comprehensive deep learning framework for MRI reconstruction