Wasserstein Patch Prior for Image Superresolution

Abstract

In this paper, we introduce a Wasserstein patch prior for superresolution of two- and three-dimensional images. Here, we assume that we have given (additionally to the low resolution observation) a reference image which has a similar patch distribution as the ground truth of the reconstruction. This assumption is e.g. fulfilled when working with texture images or material data. Then, the proposed regularizer penalizes the $W_2$-distance of the patch distribution of the reconstruction to the patch distribution of some reference image at different scales. We demonstrate the performance of the proposed regularizer by two- and three-dimensional numerical examples.

References in corpus (1)

• Multi-Resolution Data Fusion for Super Resolution Imaging

Cited by in corpus (4)

• PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization
• Generalized Normalizing Flows via Markov Chains
• WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution
• Stochastic Super-Resolution For Gaussian Textures