(Minor improvements added Dec 15 2020, see README_Update_Github.pdf)
Matlab implementation of Marchenko Pastur denoising (Veraart et al, NeuroImage 142 (2016) 394–406)
Exploiting data redundancy (PCA) and known random matrix properties (Marchenko Pastur eigenvalue distribution) to estimate and partially remove noise.
Denoising implementation by Jonas Olesen, Mark Does and Sune Jespersen for diffusion MRI data based on the algorithm presented by Veraart et al. (2016) 142, p 394-406 https://doi.org/10.1016/j.neuroimage.2016.08.016. Modified to remove mean across voxels (compute principal components of the covariance not correlation matrix).
Free to use, but please cite Veraart et al., NeuroImage (2016) 142, p 394-406 (https://doi.org/10.1016/j.neuroimage.2016.08.016) and Does, MD al. Evaluation of principal component analysis image denoising on multi‐exponential MRI relaxometry. Magn Reson Med. 2019; 81: 3503– 3514. https://doi.org/10.1002/mrm.27658
Check out our other repository, https://github.com/Neurophysics-CFIN/Tensor-MP-PCA for tensor generalization to multidimensional data!