Ccpi Denoising Gpu 3D¶
Description¶
Wrapper for CCPi-Regularisation Toolkit (GPU) for efficient 2D/3D denoising
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: "[]"
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
padding:
visibility: advanced
dtype: int
description: The amount of pixels to pad each slab of the cropped projection data.
default: "7"
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: The 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: advanced
dtype: str
options: "['SINOGRAM', 'PROJECTION', 'VOLUME_YZ', 'VOLUME_XZ', 'VOLUME_XY']"
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
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
API
-
class
CcpiDenoisingGpu3d
[source] -
get_gpu_memory
()[source]
-
nClone_datasets
()[source] The number of output datasets that have an clone - i.e. they take it in turns to be used as output in an iterative plugin.
-
nInput_datasets
()[source] The number of datasets required as input to the plugin
- Returns
Number of input datasets
-
nOutput_datasets
()[source] The number of datasets created by the plugin
- Returns
Number of output datasets
-
pre_process
()[source] This method is called immediately after base_pre_process().
-
process_frames
(data)[source] This method is called after the plugin has been created by the pipeline framework and forms the main processing step
- Parameters
data (list(np.array)) – A list of numpy arrays for each input dataset.
-
set_filter_padding
(**kwargs) Should be overridden to define how wide the frame should be for each input data set
-
setup
(**kwargs) This method is first to be called after the plugin has been created. It determines input/output datasets and plugin specific dataset information such as the pattern (e.g. sinogram/projection).
-