ccpi_denoising_gpu.rst

{% extends "plugin_template.rst" %}

{% block title %}Ccpi Denoising Gpu{% endblock %}

{% block description %} Wrapper for CCPi-Regularisation Toolkit (GPU) for efficient 2D/3D denoising. {% endblock %}

{% block parameter_yaml %}

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: "[]"

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: 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. default: "300"

time_step:

visibility: advanced dtype: float description: Time marching step, relevant for ROF_TV, LLT_ROF, NDF, Diff4th methods. default: "0.001" dependency: method: ROF_TV LLT_ROF NDF Diff4th

lipshitz_constant:

visibility: advanced dtype: int 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 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: intermediate dtype: str description: Pattern to apply this to. options: "['SINOGRAM', 'PROJECTION', 'VOLUME_XZ', 'VOLUME_XY']" default: VOLUME_XZ

{% endblock %}

{% block plugin_citations %}

Ccpi-regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms by Kazantsev, Daniil et al.

Bibtex

@article{kazantsev2019ccpi,
title={Ccpi-regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms},
author={Kazantsev, Daniil and Pasca, Edoardo and Turner, Martin J and Withers, Philip J},
journal={SoftwareX},
volume={9},
pages={317--323},
year={2019},
publisher={Elsevier}
}

Endnote

%0 Journal Article
%T Ccpi-regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms
%A Kazantsev, Daniil
%A Pasca, Edoardo
%A Turner, Martin J
%A Withers, Philip J
%J SoftwareX
%V 9
%P 317-323
%@ 2352-7110
%D 2019
%I Elsevier

Nonlinear total variation based noise removal algorithms by Rudin, Leonid I et al.

(Please use this citation if you are using the ROF_TV method

Bibtex

@article{rudin1992nonlinear,
  title={Nonlinear total variation based noise removal algorithms},
  author={Rudin, Leonid I and Osher, Stanley and Fatemi, Emad},
  journal={Physica D nonlinear phenomena},
  volume={60},
  number={1-4},
  pages={259--268},
  year={1992},
  publisher={North-Holland}
}

Endnote

%0 Journal Article
%T Nonlinear total variation based noise removal algorithms
%A Rudin, Leonid I
%A Osher, Stanley
%A Fatemi, Emad
%J Physica D nonlinear phenomena
%V 60
%N 1-4
%P 259-268
%@ 0167-2789
%D 1992
%I North-Holland

Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems by Beck, Amir et al.

(Please use this citation if you are using the FGP_TV method

Bibtex

@article{beck2009fast,
  title={Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems},
  author={Beck, Amir and Teboulle, Marc},
  journal={IEEE transactions on image processing},
  volume={18},
  number={11},
  pages={2419--2434},
  year={2009},
  publisher={IEEE}
}

Endnote

%0 Journal Article
%T Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems
%A Beck, Amir
%A Teboulle, Marc
%J IEEE transactions on image processing
%V 18
%N 11
%P 2419-2434
%@ 1057-7149
%D 2009
%I IEEE

The split Bregman method for L1-regularized problems by Goldstein, Tom et al.

(Please use this citation if you are using the SB_TV method

Bibtex

@article{goldstein2009split,
   title={The split Bregman method for L1-regularized problems},
   author={Goldstein, Tom and Osher, Stanley},
   journal={SIAM journal on imaging sciences},
   volume={2},
   number={2},
   pages={323--343},
   year={2009},
   publisher={SIAM}
 }

Endnote

%0 Journal Article
%T The split Bregman method for L1-regularized problems
%A Goldstein, Tom
%A Osher, Stanley
%J SIAM journal on imaging sciences
%V 2
%N 2
%P 323-343
%@ 1936-4954
%D 2009
%I SIAM

Total generalized variation by Bredies, Kristian et al.

(Please use this citation if you are using the TGV method

Bibtex

@article{bredies2010total,
   title={Total generalized variation},
   author={Bredies, Kristian and Kunisch, Karl and Pock, Thomas},
   journal={SIAM Journal on Imaging Sciences},
   volume={3},
   number={3},
   pages={492--526},
   year={2010},
   publisher={SIAM}
 }

Endnote

%0 Journal Article
%T Total generalized variation
%A Bredies, Kristian
%A Kunisch, Karl
%A Pock, Thomas
%J SIAM Journal on Imaging Sciences
%V 3
%N 3
%P 492-526
%@ 1936-4954
%D 2010
%I SIAM

Model-based iterative reconstruction using higher-order regularization of dynamic synchrotron data by Kazantsev, Daniil et al.

(Please use this citation if you are using the LLT_ROF method

Bibtex

@article{kazantsev2017model,
 title={Model-based iterative reconstruction using higher-order regularization of dynamic synchrotron data},
 author={Kazantsev, Daniil and Guo, Enyu and Phillion, AB and Withers, Philip J and Lee, Peter D},
 journal={Measurement Science and Technology},
 volume={28},
 number={9},
 pages={094004},
 year={2017},
 publisher={IOP Publishing}
 }

Endnote

%0 Journal Article
%T Model-based iterative reconstruction using higher-order regularization of dynamic synchrotron data
%A Kazantsev, Daniil
%A Guo, Enyu
%A Phillion, AB
%A Withers, Philip J
%A Lee, Peter D
%J Measurement Science and Technology
%V 28
%N 9
%P 094004
%@ 0957-0233
%D 2017
%I IOP Publishing

Scale-space and edge detection using anisotropic diffusion by Perona, Pietro et al.

(Please use this citation if you are using the NDF method

Bibtex

@article{perona1990scale,
   title={Scale-space and edge detection using anisotropic diffusion},
   author={Perona, Pietro and Malik, Jitendra},
   journal={IEEE Transactions on pattern analysis and machine intelligence},
   volume={12},
   number={7},
   pages={629--639},
   year={1990},
   publisher={IEEE}}

Endnote

%0 Journal Article
%T Scale-space and edge detection using anisotropic diffusion
%A Perona, Pietro
%A Malik, Jitendra
%J IEEE Transactions on pattern analysis and machine intelligence
%V 12
%N 7
%P 629-639
%@ 0162-8828
%D 1990
%I IEEE

An anisotropic fourth-order diffusion filter for image noise removal by Hajiaboli, Mohammad Reza et al.

(Please use this citation if you are using the Diff4th method

Bibtex

@article{hajiaboli2011anisotropic,
 title={An anisotropic fourth-order diffusion filter for image noise removal},
 author={Hajiaboli, Mohammad Reza},
 journal={International Journal of Computer Vision},
 volume={92},
 number={2},
 pages={177--191},
 year={2011},
 publisher={Springer}
 }

Endnote

%0 Journal Article
%T An anisotropic fourth-order diffusion filter for image noise removal
%A Hajiaboli, Mohammad Reza
%J International Journal of Computer Vision
%V 92
%N 2
%P 177-191
%@ 0920-5691
%D 2011
%I Springer

Nonlocal discrete regularization on weighted graphs, a framework for image and manifold processing by Elmoataz, Abderrahim et al.

(Please use this citation if you are using the NLTV method

Bibtex

@article{elmoataz2008nonlocal,
  title={Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing},
  author={Elmoataz, Abderrahim and Lezoray, Olivier and Bougleux, S{'e}bastien},
  journal={IEEE transactions on Image Processing},
  volume={17},
  number={7},
  pages={1047--1060},
  year={2008},
  publisher={IEEE}
}

Endnote

%0 Journal Article
%T Nonlocal discrete regularization on weighted graphs, a framework for image and manifold processing
%A Elmoataz, Abderrahim
%A Lezoray, Olivier
%A Bougleux, Sebastien
%J IEEE transactions on Image Processing
%V 17
%N 7
%P 1047-1060
%@ 1057-7149
%D 2008
%I IEEE

{% endblock %}

{% block plugin_file %}../../../../plugin_api/plugins.filters.denoising.ccpi_denoising_gpu.rst{% endblock %}