Wasserstein Patch Prior for Image Superresolution
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.
Code (1)
Similar Papers 제목 키워드 기반
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution
Exploiting image patches instead of whole images have proved to be a powerful approach to tackle various problems in image processing. Recently, Wasserstein patch priors (WPP), which are based on the comparison of the pa…
Uncertainty QuantificationVariational Depth Superresolution Using Example-Based Edge Representations
In this paper we propose a novel method for depth image superresolution which combines recent advances in example based upsampling with variational superresolution based on a known blur kernel. Most traditional depth sup…
PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization
Learning neural networks using only few available information is an important ongoing research topic with tremendous potential for applications. In this paper, we introduce a powerful regularizer for the variational mode…
Computed Tomography (CT)Exploring the Effect of Sparse Recovery on the Quality of Image Superresolution
Dictionary learning can be used for image superresolution by learning a pair of coupled dictionaries of image patches from high-resolution and low-resolution image pairs such that the corresponding pairs share the same s…
Dictionary LearningLearning Image Priors through Patch-based Diffusion Models for Solving Inverse Problems
Diffusion models can learn strong image priors from underlying data distribution and use them to solve inverse problems, but the training process is computationally expensive and requires lots of data. Such bottlenecks p…
CT ReconstructionDeblurring