  Estimating uncertainty in image-to-image networks is an important task,
particularly as such networks are being increasingly deployed in the biological
and medical imaging realms. In this paper, we introduce a new approach to this
problem based on masking. Given an existing image-to-image network, our
approach computes a mask such that the distance between the masked
reconstructed image and the masked true image is guaranteed to be less than a
specified threshold, with high probability. The mask thus identifies the more
certain regions of the reconstructed image. Our approach is agnostic to the
underlying image-to-image network, and only requires triples of the input
(degraded), reconstructed and true images for training. Furthermore, our method
is agnostic to the distance metric used. As a result, one can use $L_p$-style
distances or perceptual distances like LPIPS, which contrasts with
interval-based approaches to uncertainty. Our theoretical guarantees derive
from a conformal calibration procedure. We evaluate our mask-based approach to
uncertainty on image colorization, image completion, and super-resolution
tasks, demonstrating high quality performance on each.
