PGURE-SVT¶
PGURE-SVT (Poisson-Gaussian Unbiased Risk Estimator - Singular Value Thresholding) is an algorithm designed to denoise image sequences acquired in microscopy. It exploits the correlations between consecutive frames to form low-rank matrices, which are then recovered using a technique known as nuclear norm minimization. An unbiased risk estimator for mixed Poisson-Gaussian noise is used to automate the selection of the regularization parameter, while robust noise and motion estimation maintain broad applicability to many different types of microscopy.
If you use this code in a publication, please cite our work:
T. Furnival, R. K. Leary and P. A. Midgley, “Denoising time-resolved microscopy sequences with singular value thresholding”, Ultramicroscopy, vol. 178, pp. 112–124, 2017. DOI: 10.1016/j.ultramic.2016.05.005.
Contents¶
Quickstart¶
To install pgure-svt in a conda environment (Linux and MacOS only)
from conda-forge:
conda install pgure-svt -c conda-forge
For further details (including building from source), see the installation user guide.
Once installed, you can use pgure-svt as below:
import numpy as np
from pguresvt import SVT
# Example dataset has dimensions (nx, ny, nt),
# in this case a 64x64px video with 25 frames
X = np.random.randn(64, 64, 25)
# Initialize the algorithm
# with default parameters
svt = SVT()
# Run the algorithm on the data X
svt.denoise(X)
# Get the denoised data Y
Y = svt.Y_
Contributing¶
All contributions to PGURE-SVT are welcome!
There are many ways to contribute to PGURE-SVT, with the most common ones being contribution of code, documentation or examples to the project. You can also help by by answering queries on the issue tracker, investigating bugs, and reviewing other pull requests, or by reporting any issues you are facing. Please use the GitHub issue tracker to report bugs.
Lastly, you can contribute by helping to spread the word about PGURE-SVT: reference the project from your blog and articles, link to it from your website, or simply star it in GitHub to say “I use it”.