Wasserstein Patch Prior for Image Superresolution

This repository contains the implementations corresponding to the paper [1] availible at https://doi.org/10.1109/TCI.2022.3199600. A preprint can be found at https://arxiv.org/abs/2109.12880. Please cite the paper, if you use the code.

The repository implements image superresolution for textures or material microstructures. The file wgenpatex.py is adapted from the implementation of [2] availible at https://github.com/ahoudard/wgenpatex.

The used images of material microstructures have been acquired in the frame of the EU Horizon 2020 Marie Sklodowska-Curie Actions Innovative Training Network MUMMERING (MUltiscale, Multimodal and Multidimensional imaging for EngineeRING, Grant Number 765604) at the beamline TOMCAT of the SLS by A. Saadaldin, D. Bernard, and F. Marone Welford.

For questions and bug reports, please contact Johannes Hertrich ([email protected]).

Requirements and Usage

The code is written in Python using

• PyTorch 1.9.0
• Pykeops 1.5
• scipy 1.6.2
• Scikit-image 0.18.1
• Numpy

Usually the code is also compatible with some other versions of the corresponding packages.

The scripts run_diam.py and run_FS.py reproduce the 2D examples of Section 4.2 of the paper [1]. The core function is the function optim_synthesis from the file wgenpatex.py. A short discription of each script/function can be found in the corresponding header.

References

[1] J. Hertrich, A. Houdard and C. Redenbach.
Wasserstein Patch Prior for Image Superresolution.
IEEE Transactions on Computational Imaging, 2022.

[2] A. Houdard, A. Leclaire, N. Papadakis and J. Rabin.
Wasserstein Generative Models for Patch-based Texture Synthesis.
ArXiv Preprin#2007.03408