"MPPCA": 4d image denoising and noise map estimation by exploiting data redundancy in the PCA domain using universal properties of the eigenspectrum of random covariance matrices, i.e. Marchenko Pastur distribution
MATLAB:
[Signal, Sigma] = MPdenoising(data, mask, kernel, sampling)
output:
- Signal: [x, y, z, N] denoised data matrix
- Sigma: [x, y, z] noise map
input:
- data: [x, y, z, M] data matrix
- mask: (optional) region-of-interest [boolean]
- kernel: (optional) window size, typically in order of [5 x 5 x 5]
- sampling:
1. full: sliding window (default for noise map estimation, i.e. [Signal, Sigma] = MPdenoising(...) )
2. fast: block processing (default for denoising, i.e. [Signal] = MPdenoising(...))
PYTHON:
import mpdenoise as mp
imgdn, sigma, nparameters = mp.denoise(img, kernel='5,5,5)
output:
- Signal [x, y, z, N] denoised data matrix
- Sigma [x, y, z] noise map
- N parameters [x, y, z] significant principal component map
input:
- data: [x, y, z, N] data matrix
- kernel: (optional) window size, typically in order of [5 x 5 x 5]
Authors: Jelle Veraart (jelle.veraart@nyumc.org), Ben Ades-Aron (Benjamin.Ades-Aron@nyulangone.org)
Copyright (c) 2016 New York Universit and University of Antwerp
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REFERENCES
Veraart, J.; Fieremans, E. & Novikov, D.S. Diffusion MRI noise mapping
using random matrix theory Magn. Res. Med., 2016, early view, doi:
10.1002/mrm.26059