A library for fast FFT-computed global mutual information-based rigid alignment using the GPU.
Requirements:
Python3.8.8, PyTorch 1.8.1, numpy, torchvision, scipy, sklearn
(CUDA-compatible GPU for GPU acceleration.)
Related to the article (if you use this code, please cite it):
Johan Öfverstedt, Joakim Lindblad, and Nataša Sladoje. Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment. Pattern Recognition Letters, Vol. 159, pp. 196-203, 2022. doi:10.1016/j.patrec.2022.05.022
Preprint: https://arxiv.org/abs/2106.14699
To use the library, please see the included example script "example.py".
Main author of the code:
Johan Öfverstedt
python -m venv ./venv
. ./venv/bin/activate
pip install -r requirements.txt -r Learn2Reg/requirements.txt
#Download the Dataset for *TASK 3: COMULISglobe SHG-BF*
unzip COMULISSHGBF.zip
#Run globalign/CMIF registration using a rather coarse (fast) search
python Learn2Reg/COMULISSHGBF_2024.py
Validation displacement fields are saved to the directory output