2
"name" : "ProjectedLandweberDeconvolutionImageFilter",
3
"template_code_filename" : "ImageFilter",
4
"template_test_filename" : "ImageFilter",
5
"number_of_inputs" : 2,
6
"doc" : "Some global documentation",
7
"pixel_types" : "BasicPixelIDTypeList",
9
"sitkBoundaryConditions.hxx"
11
"custom_set_input" : "filter->SetInput( image1 ); filter->SetKernelImage( image2 );",
17
"briefdescriptionSet" : "",
18
"detaileddescriptionSet" : "Set the relaxation factor.",
19
"briefdescriptionGet" : "",
20
"detaileddescriptionGet" : "Get the relaxation factor."
23
"name" : "NumberOfIterations",
26
"briefdescriptionSet" : "",
27
"detaileddescriptionSet" : "Set the number of iterations.",
28
"briefdescriptionGet" : "",
29
"detaileddescriptionGet" : "Get the number of iterations."
36
"briefdescriptionSet" : "",
37
"detaileddescriptionSet" : "Normalize the output image by the sum of the kernel components\n",
38
"briefdescriptionGet" : "",
39
"detaileddescriptionGet" : ""
42
"name" : "BoundaryCondition",
45
"ZERO_FLUX_NEUMANN_PAD",
48
"default" : "itk::simple::ProjectedLandweberDeconvolutionImageFilter::ZERO_FLUX_NEUMANN_PAD",
49
"custom_itk_cast" : "nsstd::auto_ptr< ImageBoundaryCondition< InputImageType > > bc( CreateNewBoundaryConditionInstance< Self, FilterType >( m_BoundaryCondition ) ); filter->SetBoundaryCondition( bc.get() );\n"
52
"name" : "OutputRegionMode",
57
"default" : "itk::simple::ProjectedLandweberDeconvolutionImageFilter::SAME",
58
"itk_type" : "typename FilterType::OutputRegionModeType"
64
"description" : "Projected Landweber deconvolution of image blurred with a Gaussian kernel",
67
"parameter" : "Normalize",
69
"python_value" : "True",
73
"tolerance" : "0.0001",
75
"Input/DeconvolutionInput.nrrd",
76
"Input/DeconvolutionKernel.nrrd"
80
"briefdescription" : "Deconvolve an image using the projected Landweber deconvolution algorithm.",
81
"detaileddescription" : "This filter performs the same calculation per iteration as the LandweberDeconvolutionImageFilter . However, at each iteration, negative pixels in the intermediate result are projected (set) to zero. This is useful if the solution is assumed to always be non-negative, which is the case when dealing with images formed by counting photons, for example.\n\nThis code was adapted from the Insight Journal contribution:\n\n\"Deconvolution: infrastructure and reference algorithms\" by Gaetan Lehmann https://hdl.handle.net/10380/3207 \n\n\\author Gaetan Lehmann, Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France \n\nCory Quammen, The University of North Carolina at Chapel Hill\n\n\\see IterativeDeconvolutionImageFilter \n\n\\see RichardsonLucyDeconvolutionImageFilter \n\n\\see LandweberDeconvolutionImageFilter",
82
"itk_module" : "ITKDeconvolution",
83
"itk_group" : "Deconvolution"
b'\\ No newline at end of file'