Invertible Learned Primal-Dual
Abstract
We propose invertible Learned Primal-Dual as a method for tomographic image reconstruction. This is a learned iterative method based on the Learned Primal-Dual neural network architecture, which incorporates ideas from invertible neural networks. The invertibility significantly reduces the GPU memory footprint of the Learned Primal-Dual architecture, thus making it applicable to 3D tomographic reconstruction as demonstrated in the experiments.
Cite
Text
Rudzusika et al. "Invertible Learned Primal-Dual." NeurIPS 2021 Workshops: Deep_Inverse, 2021.Markdown
[Rudzusika et al. "Invertible Learned Primal-Dual." NeurIPS 2021 Workshops: Deep_Inverse, 2021.](https://mlanthology.org/neuripsw/2021/rudzusika2021neuripsw-invertible/)BibTeX
@inproceedings{rudzusika2021neuripsw-invertible,
title = {{Invertible Learned Primal-Dual}},
author = {Rudzusika, Jevgenija and Bajic, Buda and Öktem, Ozan and Schönlieb, Carola-Bibiane and Etmann, Christian},
booktitle = {NeurIPS 2021 Workshops: Deep_Inverse},
year = {2021},
url = {https://mlanthology.org/neuripsw/2021/rudzusika2021neuripsw-invertible/}
}