ImWIP: Image Warping for Inverse Problems
ImWIP provides efficient, matrix-free and GPU accelerated implementations of image warping operators, in Python and C++. The goal of this package is to enable the use of image warping in inverse problems. This requires two extra operations on top of regular image warping: adjoint image warping (to solve for images) and differentiated image warping (to solve for the deformation field).
ImWIP heavily relies on CUDA kernels for efficient parallelization. Therfore a CUDA enabeled GPU is required. Furthermore, the following python dependencies are needed, which can be easily installed using conda, and get the package working using the numba/CUDA backend.
- python >= 3.8
- numpy
- scipy
- numba
- pylops
- tqdm
- cython
For conda users, these dependencies can be automatically installed from the environment.yml file in the root of this project. For example by running
conda env create
conda activate imwipThere is also a C++/CUDA backend, which is a bit faster than the numba backend, and it can be accessed from any language that supports a C interface functions. It will automatically compile on linux systems where the nvcc compiler is available.
If git is installed, simply run
pip install git+https://github.com/RendersJens/ImWIP.gitOtherwise, download this repository and run pip in the root folder of the project:
pip install .Full documentation is available on https://imwip.readthedocs.io/
If you use ImWIP in your research, please cite
Jens Renders, Ben Jeurissen, Anh-Tuan Nguyen, Jan De Beenhouwer, Jan Sijbers, ImWIP: Open-source image warping toolbox with adjoints and derivatives, SoftwareX, Volume 24, 2023, 101524
BibTex:
@article{renders2023,
title = {ImWIP: Open-source image warping toolbox with adjoints and derivatives},
author = {Jens Renders and Ben Jeurissen and Anh-Tuan Nguyen and Jan {De Beenhouwer} and Jan Sijbers},
journal = {SoftwareX},
volume = {24},
pages = {101524},
year = {2023},
issn = {2352-7110},
doi = {https://doi.org/10.1016/j.softx.2023.101524},
}