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MPdenoising.m
README.md

README.md

"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

  [Signal, Sigma] = MPdenoising(data, mask, kernel, sampling)
       output:
           - Signal: [x, y, z, M] 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(...))
 
  Authors: Jelle Veraart (jelle.veraart@nyumc.org)
 Copyright (c) 2016 New York Universit and University of Antwerp
       
      Permission is hereby granted, free of charge, to any non-commercial entity
      ('Recipient') obtaining a copy of this software and associated
      documentation files (the 'Software'), to the Software solely for
      non-commercial research, including the rights to use, copy and modify the
      Software, subject to the following conditions: 
       
        1. The above copyright notice and this permission notice shall be
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        2. THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,
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      4. Neither anything contained herein nor the delivery of the Software to
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        5. The Software may only be used for non-commercial research and may not
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        6. Any publication by Recipient of research involving the Software shall
      cite the references listed below.
 
 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