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

• URL: http://arxiv.org/abs/2505.09528v1
• Date: Wed, 14 May 2025 16:23:26 GMT
• Title: Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems
• Authors: Jeffrey Wen, Rizwan Ahmad, Philip Schniter,
• Abstract summary: 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.<n>We demonstrate our approach on image denoising and accelerated magnetic resonance imaging (MRI) problems.
• Score: 11.393603788068777
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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