Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems
Image restoration problems are often ill-posed, leading to significant uncertainty in reconstructed images. Accurately quantifying this uncertainty is essential for the reliable interpretation of reconstructed images. This work develops a self-supervised conformal prediction method for Poisson imaging problems, leveraging a Poisson unbiased risk estimator to calibrate uncertainty without ground-truth images. The resulting self-calibrating conformal prediction approach is applicable to any Poisson linear imaging problem that is ill-conditioned, and is particularly effective when combined with modern self-supervised image restoration techniques trained directly on measurement data.
