DIRECT is a Python, end-to-end pipeline for solving Inverse Problems emerging in Imaging Processing. It is built with PyTorch and stores state-of-the-art Deep Learning imaging inverse problem solvers such as denoising, dealiasing and reconstruction. By defining a base forward linear or non-linear operator, DIRECT can be used for training models for recovering images such as MRIs from partially observed or noisy input data.
DIRECT stores inverse problem solvers such as the Learned Primal Dual algorithm and Recurrent Inference Machine, which were part of the winning solution in Facebook & NYUs FastMRI challenge in 2019 and the Calgary-Campinas MRI reconstruction challenge at MIDL 2020. For a full list of the baselines currently implemented in DIRECT see here.
See install.md.
See getting_started.md, check out the documentation. In the projects folder examples are given on how to train models on public datasets.
- Recurrent Variational Network (RecurrentVarNet)
- Recurrent Inference Machine (RIM)
- End-to-end Variational Network (VarNet)
- Learned Primal Dual Network (LDPNet)
- X-Primal Dual Network (XPDNet)
- KIKI-Net
- U-Net
- Joint-ICNet
- AIRS Medical fastmri model (MultiDomainNet)
We provide a set of baseline results and trained models in the DIRECT Model Zoo.
DIRECT is released under the Apache 2.0 License.
If you use DIRECT in your own research, or want to refer to baseline results published in the DIRECT Model Zoo, please use the following BiBTeX entry:
@misc{DIRECTTOOLKIT,
author = {Yiasemis, George and Moriakov, Nikita and Karkalousos, Dimitrios and Caan, Matthan and Teuwen, Jonas},
title = {DIRECT: Deep Image REConstruction Toolkit},
howpublished = {\url{https://github.com/directgroup/direct}},
year = {2021}
}