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

Jeffrey Wen, Rizwan Ahmad, Philip Schniter

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.

Submission Length: Long submission (more than 12 pages of main content)

Changes Since Last Submission:

- Used the camera ready format with author names, date, and OpenReview link
- Removed colored text indicating previous revisions

Code: https://github.com/jwen307/quality_uq

Assigned Action Editor: ~Fred_Roosta1

Submission Number: 4368