WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for
Image Superresolution
- URL: http://arxiv.org/abs/2201.08157v1
- Date: Thu, 20 Jan 2022 13:04:19 GMT
- Title: WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for
Image Superresolution
- Authors: Fabian Altekr\"uger, Johannes Hertrich
- Abstract summary: WPPNets are CNNs trained by a new unsupervised loss function for image superresolution of materials microstructures.
We show that WPPNets are much more stable under inaccurate knowledge or perturbations of the forward operator.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 image. We demonstrate by numerical examples that WPPNets
outperform other methods with similar assumptions. In particular, we show that
WPPNets are much more stable under inaccurate knowledge or perturbations of the
forward operator. This enables us to use them in real-world applications, where
neither a large database of registered data nor the exact forward operator are
given.
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