The Maximum Spacing Noise Estimation in Single-coil Background MRI Data

Matlab implementations of the maximum spacing noise estimators
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Updated 29 Dec 2014

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New noise estimation technique in single-coil background MRI data based on the maximum spacing estimation (MSP) principle for Rayleigh distributed data.
This MATLAB codes include implementations of the MSP algorithm and its variants:
- MSP estimator based on Kullback-Leibler divergence,
- MSP estimator based on J-divergence,
- MSP estimator based on Renyi divergence,
- MSP estimator based on Vajda's divergence.

Moreover, we supply MATLAB implementations of the previous literature reports:
- Aja's method,
- Brummer's method,
- Brummer-Aja's method,
- Chang's method,
- Maximum likelihood principle,
- Sijbers's method,
- Sijbers-Aja's method.

IMPORTANT NOTICE!
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You can use these codes freely in your research/work, however, cite the following paper, please:

Pieciak, T. The Maximum Spacing Noise Estimation in Single-coil Background MRI Data. In: Image Processing (ICIP), 2014 21th IEEE International Conference on. IEEE, 2014. p. 1743-1747.

The draft of the paper can be downloaded at:
http://home.agh.edu.pl/pieciak/publikacje_pieciak/2014_ICIP_Pieciak.pdf

Cite As

Tomasz Pieciak (2024). The Maximum Spacing Noise Estimation in Single-coil Background MRI Data (https://www.mathworks.com/matlabcentral/fileexchange/45236-the-maximum-spacing-noise-estimation-in-single-coil-background-mri-data), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2012b
Compatible with any release
Platform Compatibility
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Version Published Release Notes
1.2.0.0

Description update.

1.0.0.0