Iterative Ccpi Denoising¶

Description¶

A wrapper for CCPi-Regularisation Toolkit (CPU) for efficient 2D denoising with changing patterns

Parameters

in_datasets:
    visibility: datasets
    dtype: "[list[],list[str]]"
    description:
        summary: A list of the dataset(s) to process.
        verbose: A list of strings, where each string gives the name of a dataset that was either specified by a loader plugin or created as output to a previous plugin.  The length of the list is the number of input datasets requested by the plugin.  If there is only one dataset and the list is left empty it will default to that dataset.
    default: "[]"

out_datasets:
    visibility: datasets
    dtype: "[list[],list[str]]"
    description:
        summary: A list of the dataset(s) to create.
        verbose: A list of strings, where each string is a name to be assigned to a dataset output by the plugin. If there is only one input dataset and one output dataset and the list is left empty, the output will take the name of the input dataset. The length of the list is the number of output datasets created by the plugin.
    default: "[]"

plugin_iterations:
    visibility: basic
    dtype: int
    description:
        summary: "The number of Savu's plugin outer iterations"
    default: "5"

method:
    visibility: advanced
    dtype: str
    options: "['ROF_TV', 'PD_TV', 'FGP_TV', 'SB_TV', 'NLTV', 'TGV', 'LLT_ROF', 'NDF', 'Diff4th']"
    description:
        summary: The denoising method
        verbose: Variational denoising algorithms can be used to filter the data while preserving the important features
        options:
            ROF_TV: Rudin-Osher-Fatemi Total Variation model
            PD_TV: Primal-Dual Total variation model
            FGP_TV: Fast Gradient Projection Total Variation model
            SB_TV: Split Bregman Total Variation model
            LLT_ROF: Lysaker, Lundervold and Tai model combined with Rudin-Osher-Fatemi
            NDF: Nonlinear/Linear Diffusion model (Perona-Malik, Huber or Tukey)
            TGV: Total Generalised Variation
            NLTV: Non Local Total Variation
            Diff4th: Fourth-order nonlinear diffusion model
    default: FGP_TV

reg_parameter:
    visibility: basic
    dtype: float
    description:
        summary: The regularisation (smoothing) parameter. The higher the value, the stronger the smoothing effect
        range: Recommended between 0 and 0.001
    default: "1e-05"

max_iterations:
    visibility: basic
    dtype: int
    description:
        summary: Total number of regularisation iterations.  The smaller the number of iterations, the smaller the effect of the filtering is.  A larger number will affect the speed of the algorithm.
        range: Recommended value dependent upon method.
    default:
        method:
            ROF_TV: "2000"
            FGP_TV: "500"
            PD_TV: "500"
            SB_TV: "100"
            LLT_ROF: "2000"
            NDF: "2000"
            Diff4th: "1000"
            TGV: "500"
            NLTV: "5"

time_step:
    visibility: advanced
    dtype: float
    dependency:
        regularisation_method:
            ROF_TV
            LLT_ROF
            NDF
            Diff4th
    description:
        summary: Time marching parameter for convergence of explicit schemes
        verbose: the time step constant defines the speed of convergence, the larger values can lead to divergence
        range: Recommended between 0.0001 and 0.003
    default: "0.003"

lipshitz_constant:
    visibility: advanced
    dtype: float
    description: TGV method, Lipshitz constant.
    default: "12"
    dependency:
        method: TGV

alpha1:
    visibility: advanced
    dtype: float
    description: TGV method, parameter to control the 1st-order term.
    default: "1.0"
    dependency:
        method: TGV

alpha0:
    visibility: advanced
    dtype: float
    description: TGV method, parameter to control the 2nd-order term.
    default: "2.0"
    dependency:
        method: TGV

reg_parLLT:
    visibility: advanced
    dtype: float
    dependency:
        method: LLT_ROF
    description: LLT-ROF method, parameter to control the 2nd-order term.
    default: "0.05"

penalty_type:
    visibility: advanced
    dtype: str
    options: "['huber', 'perona', 'tukey', 'constr', 'constrhuber']"
    description:
        summary: Penalty type
        verbose: Nonlinear/Linear Diffusion model (NDF) specific penalty type.
        options:
            huber: Huber
            perona: Perona-Malik model
            tukey: Tukey
            constr: None
            constrhuber: None
    dependency:
        method: NDF
    default: huber

edge_par:
    visibility: advanced
    dtype: float
    dependency:
        method:
            NDF
            Diff4th
    description: NDF and Diff4th methods, noise magnitude parameter.
    default: "0.01"

tolerance_constant:
    visibility: advanced
    dtype: float
    description: Tolerance constant to stop iterations earlier.
    default: "0.0"

pattern:
    visibility: advanced
    dtype: str
    description: Pattern to apply this to.
    default: VOLUME_XZ

Key

visibility: The visibility level of the parameter
dtype: The type of data
description: A short explanation of the parameter
default: The default value
options: A list of permitted values
dependency: The name of the parameter and value which this parameter depends upon
range: A guide for the range of the parameter