Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems
Keywords: Inverse Problems, Conformal Prediction, Uncertainty Quantification, MRI
TL;DR: For imaging inverse problems, we propose methods to construct conformal bounds on recovered-image quality metrics like PSNR, SSIM, LPIPS, etc., without access to the true image.
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.
Supplementary Material: zip
Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
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Submission Number: 4824
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