Wasserstein Patch Prior for Image Superresolution
arXiv:2109.12880 · doi:10.1109/TCI.2022.3199600
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 -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 (12)
- Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
- BM3D Frames and Variational Image Deblurring
- CNN-Based Projected Gradient Descent for Consistent Image Reconstruction
- Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation
- Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems
- A Proximal Decomposition Method for Solving Convex Variational Inverse Problems
- Stochastic Optimization for Large-scale Optimal Transport
- Convolutional Proximal Neural Networks and Plug-and-Play Algorithms
- A Parallel Douglas Rachford Algorithm for Minimizing ROF-like Functionals on Images with Values in Symmetric Hadamard Manifolds
- Multi-Resolution Data Fusion for Super Resolution Imaging
- Alternatives to the EM Algorithm for ML-Estimation of Location, Scatter Matrix and Degree of Freedom of the Student- Distribution
- The basins of attraction of the global minimizers of non-convex inverse problems with low-dimensional models in infinite dimension