  One of the key limitations in conventional deep learning based image
reconstruction is the need for registered pairs of training images containing a
set of high-quality groundtruth images. This paper addresses this limitation by
proposing a novel unsupervised deep registration-augmented reconstruction
method (U-Dream) for training deep neural nets to reconstruct high-quality
images by directly mapping pairs of unregistered and artifact-corrupted images.
The ability of U-Dream to circumvent the need for accurately registered data
makes it widely applicable to many biomedical image reconstruction tasks. We
validate it in accelerated magnetic resonance imaging (MRI) by training an
image reconstruction model directly on pairs of undersampled measurements from
images that have undergone nonrigid deformations.
