denoising_nlmeans
Denoise a dataset with the Non Local Means algorithm.
In order to denoise, we have three methods to estimate the noise available with their requirements:
- Sigma (needs: sigma)
- Basic sigma (needs: ncoils, optional: mask_sigma or sigma_from_all_voxels)
- Piesno (needs: ncoils)
Keywords : nifti, denoising, nlmeans, scilpy
Inputs
Section titled “Inputs”Input 1
Section titled “Input 1”Format : tuple val(meta), path(image), path(mask), path(mask_sigma)
| Type | Description | Mandatory | Pattern | |
|---|---|---|---|---|
| meta | map | Groovy Map containing sample information e.g. [ id:'test', single_end:false ] | True | |
| image | file | Nifti image file to denoise | True | *.{nii,nii.gz} |
| mask | file | Nifti image file used to mask the input image | False | *.{nii,nii.gz} |
| mask_sigma | file | Nifti binary image file used to mask the input image for —basic_sigma estimation. Only the data inside the mask will be used to estimate sigma | False | *.{nii,nii.gz} |
Outputs
Section titled “Outputs”Format : tuple val(meta), path(*__denoised.nii.gz)
| Type | Description | Mandatory | Pattern | |
|---|---|---|---|---|
| meta | map | Groovy Map containing sample information e.g. [ id:'test', single_end:false ] | True | |
| *__denoised.nii.gz | file | Denoised Nifti image file | True | *_denoised.{nii,nii.gz} |
piesno_mask
Section titled “piesno_mask”Format : tuple val(meta), path(*__piesno_mask.nii.gz)
| Type | Description | Mandatory | Pattern | |
|---|---|---|---|---|
| meta | map | Groovy Map containing sample information e.g. [ id:'test', single_end:false ] | True | |
| *__piesno_mask.nii.gz | file | Piesno mask Nifti image file | True | *__piesno_mask.{nii,nii.gz} |
versions
Section titled “versions”Format : path(versions.yml)
| Type | Description | Mandatory | Pattern | |
|---|---|---|---|---|
| versions.yml | file | File containing software versions | True | versions.yml |
Arguments (see process.ext)
Section titled “Arguments (see process.ext)”| Type | Description | Default | Choices | |
|---|---|---|---|---|
| sigma | float | Provide your own standard deviation of the noise. | ||
| basic_sigma | bool | Estimate sigma using Use dipy’s basic estimation of sigma. | ||
| piesno | bool | Estimate sigma using Piesno’s method. If data is 4D, the noise is estimated for each slice (3rd dimension). | ||
| gaussian | bool | If your data contains gaussian noise, set this option. Otherwise, Rician is assumed. | ||
| sigma_from_all_voxels | bool | If set, all voxels are used for the —basic_sigma estimation, even zeros. | ||
| save_piesno_mask | bool | If set with piesno option, it will save the piesno mask. | ||
| ncoils | int | Number of coils used for the acquisition | 1 |
| Description | DOI | |
|---|---|---|
| scilpy | The Sherbrooke Connectivity Imaging Lab (SCIL) Python dMRI processing toolbox. |
Authors
Section titled “Authors”Last updated : 2025-10-30