为图像逆问题提供可保证的图像质量边界，提升医疗等安全场景可靠性
Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems
- 结合置信预测与近似后验采样，构建可验证的质量边界
- 在去噪和加速MRI任务中实现PSNR、SSIM等指标的可靠估计
- 适合关注图像重建可信度的医疗影像研究者
在图像逆问题中，我们希望知道恢复图像与真实图像在全参考图像质量（FRIQ）指标（如PSNR、SSIM、LPIPS）上的接近程度。这在医疗成像等安全关键应用中尤为重要，例如，若发现SSIM较差，可避免昂贵的误诊。但由于真实图像未知，直接计算FRIQ具有挑战性。本文结合置信预测与近似后验采样，构建了在用户指定误差概率下成立的FRIQ边界。我们在图像去噪和加速磁共振成像（MRI）问题上验证了该方法的有效性。代码已公开于https://github.com/jwen307/quality_uq。
原文摘要 · Abstract (English)
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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