WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution. (arXiv:2201.08157v1 [cs.CV])

We introduce WPPNets, which are CNNs trained by a new unsupervised loss
function for image superresolution of materials microstructures. Instead of
requiring access to a large database of registered high- and low-resolution
images, we only assume to know a large database of low resolution images, the
forward operator and one high-resolution reference image. Then, we propose a
loss function based on the Wasserstein patch prior which measures the
Wasserstein-2 distance between the patch distributions of the predictions and
the reference …

arxiv cv training unsupervised