Total Deep Variation Regularizer
Here, we showcase the total deep variation (TDV) regularizer introduced in
- E. Kobler, A. Effland, K. Kunisch, and T. Pock, Total deep variation for linear inverse problems, in IEEE Conference on Computer Vision and Pattern Recognition, 2020.
- E. Kobler, A. Effland, K. Kunisch, and T. Pock, Total Deep Variation: A Stable Regularizer for Inverse Problems, arXiv preprint arXiv:2006.08789.
If you use this code please cite:
@InProceedings{KoEf20,
Title = {Total Deep Variation for Linear Inverse Problems},
Author = {Kobler, Erich and Effland, Alexander and Kunisch, Karl and Pock, Thomas},
Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
Year = {2020}
}Overview
.
+-- ddr : data driven regularizers module
| +-- conv.py : implementation of forward/backward convolution operators
| +-- regularizer.py : interface for regularizers
| +-- tdv.py : implementation of the TDV regularizer
+-- data : sample images
+-- checkpoints : pytorch checkpoint files
+-- figures : figures for plotting
+-- denoise.py : simple script to run gray-scale/color denoising
+-- eigenfunctions.py : visualization of an eigenfunction of the TDV regularizer
Requirements
- numpy
- imageio
- pytorch
- scikit-image
- optox
- matplotlib
Gaussian Image Denoising
Single Image Super-resolution
Non-linear Eigenfunction Analysis
