MedianFilter3d
Applies a median operator, which is a non-linear smoothing filter, on a three-dimensional image.
Access to parameter description
For an introduction to image filters: section Images Filtering.
This algorithm uses morphological operators to set the voxel value to the median for the defined neighborhood. This filter is particularly effective for removing speckle noise and salt and pepper noise (impulsive noise) without affecting edges.
The median filter usually works well with images containing non-Gaussian noise and/or very small artifacts. It does not cause blurring to the same extent as the Box Filter, but takes longer to execute.
The gray levels of all voxels of the considered neighborhood are sorted from the smallest value to the largest one. The central voxel in the sort is then the median value (the value for which there are as many lower gray levels as higher ones). The process can be iterated.
Considering the array:
$$\begin{bmatrix} 1 & 2 & 3 \\ 4 & 5 & 6 \\ 7 & 8 & 9 \end{bmatrix} \begin{bmatrix} 12 & 17 & 15 \\ 20 & 14 & 16 \\ 18 & 19 & 14 \end{bmatrix} \begin{bmatrix} 20 & 21 & 22 \\ 23 & 24 & 25 \\ 26 & 27 & 28 \end{bmatrix} $$ The sorted gray level values are: [1 2 3 4 5 6 7 8 9 12 14 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28]. The median value is 16, and it is assigned to the output voxel.
See also
See related example
Access to parameter description
For an introduction to image filters: section Images Filtering.
This algorithm uses morphological operators to set the voxel value to the median for the defined neighborhood. This filter is particularly effective for removing speckle noise and salt and pepper noise (impulsive noise) without affecting edges.
The median filter usually works well with images containing non-Gaussian noise and/or very small artifacts. It does not cause blurring to the same extent as the Box Filter, but takes longer to execute.
The gray levels of all voxels of the considered neighborhood are sorted from the smallest value to the largest one. The central voxel in the sort is then the median value (the value for which there are as many lower gray levels as higher ones). The process can be iterated.
Considering the array:
$$\begin{bmatrix} 1 & 2 & 3 \\ 4 & 5 & 6 \\ 7 & 8 & 9 \end{bmatrix} \begin{bmatrix} 12 & 17 & 15 \\ 20 & 14 & 16 \\ 18 & 19 & 14 \end{bmatrix} \begin{bmatrix} 20 & 21 & 22 \\ 23 & 24 & 25 \\ 26 & 27 & 28 \end{bmatrix} $$ The sorted gray level values are: [1 2 3 4 5 6 7 8 9 12 14 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28]. The median value is 16, and it is assigned to the output voxel.
See also
See related example
Function Syntax
This function returns outputImage.
// Function prototype
std::shared_ptr< iolink::ImageView > medianFilter3d( std::shared_ptr< iolink::ImageView > inputImage, uint32_t kernelRadius, MedianFilter3d::KernelMode kernelMode, MedianFilter3d::SearchMode searchMode, std::shared_ptr< iolink::ImageView > outputImage = nullptr );
This function returns outputImage.
// Function prototype. median_filter_3d(input_image: idt.ImageType, kernel_radius: int = 3, kernel_mode: MedianFilter3d.KernelMode = MedianFilter3d.KernelMode.CUBE, search_mode: MedianFilter3d.SearchMode = MedianFilter3d.SearchMode.AUTOMATIC, output_image: idt.ImageType = None) -> idt.ImageType
This function returns outputImage.
// Function prototype. public static IOLink.ImageView MedianFilter3d( IOLink.ImageView inputImage, UInt32 kernelRadius = 3, MedianFilter3d.KernelMode kernelMode = ImageDev.MedianFilter3d.KernelMode.CUBE, MedianFilter3d.SearchMode searchMode = ImageDev.MedianFilter3d.SearchMode.AUTOMATIC, IOLink.ImageView outputImage = null );
Class Syntax
Parameters
Parameter Name | Description | Type | Supported Values | Default Value | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
inputImage |
The input image. | Image | Binary, Label, Grayscale or Multispectral | nullptr | |||||||
kernelRadius |
The half size of the structuring element. A cube structuring element always has an odd side length (3x3x3, 5x5x5, etc.) which is defined by twice the kernel radius + 1. | UInt32 | >=1 | 3 | |||||||
searchMode |
Handles all types of methods for finding the median.
|
Enumeration | AUTOMATIC | ||||||||
kernelMode |
The structuring element shape and application mode.
|
Enumeration | CUBE | ||||||||
outputImage |
The output image. Its dimensions and type are forced to the same values as the input. | Image | nullptr |
Parameter Name | Description | Type | Supported Values | Default Value | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
input_image |
The input image. | image | Binary, Label, Grayscale or Multispectral | None | |||||||
kernel_radius |
The half size of the structuring element. A cube structuring element always has an odd side length (3x3x3, 5x5x5, etc.) which is defined by twice the kernel radius + 1. | uint32 | >=1 | 3 | |||||||
search_mode |
Handles all types of methods for finding the median.
|
enumeration | AUTOMATIC | ||||||||
kernel_mode |
The structuring element shape and application mode.
|
enumeration | CUBE | ||||||||
output_image |
The output image. Its dimensions and type are forced to the same values as the input. | image | None |
Parameter Name | Description | Type | Supported Values | Default Value | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
inputImage |
The input image. | Image | Binary, Label, Grayscale or Multispectral | null | |||||||
kernelRadius |
The half size of the structuring element. A cube structuring element always has an odd side length (3x3x3, 5x5x5, etc.) which is defined by twice the kernel radius + 1. | UInt32 | >=1 | 3 | |||||||
searchMode |
Handles all types of methods for finding the median.
|
Enumeration | AUTOMATIC | ||||||||
kernelMode |
The structuring element shape and application mode.
|
Enumeration | CUBE | ||||||||
outputImage |
The output image. Its dimensions and type are forced to the same values as the input. | Image | null |
Object Examples
auto foam = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam.vip" ); MedianFilter3d medianFilter3dAlgo; medianFilter3dAlgo.setInputImage( foam ); medianFilter3dAlgo.setKernelRadius( 3 ); medianFilter3dAlgo.setKernelMode( MedianFilter3d::KernelMode::CUBE ); medianFilter3dAlgo.setSearchMode( MedianFilter3d::SearchMode::AUTOMATIC ); medianFilter3dAlgo.execute(); std::cout << "outputImage:" << medianFilter3dAlgo.outputImage()->toString();
foam = imagedev.read_vip_image(imagedev_data.get_image_path("foam.vip")) median_filter_3d_algo = imagedev.MedianFilter3d() median_filter_3d_algo.input_image = foam median_filter_3d_algo.kernel_radius = 3 median_filter_3d_algo.kernel_mode = imagedev.MedianFilter3d.CUBE median_filter_3d_algo.search_mode = imagedev.MedianFilter3d.AUTOMATIC median_filter_3d_algo.execute() print("output_image:", str(median_filter_3d_algo.output_image))
ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" ); MedianFilter3d medianFilter3dAlgo = new MedianFilter3d { inputImage = foam, kernelRadius = 3, kernelMode = MedianFilter3d.KernelMode.CUBE, searchMode = MedianFilter3d.SearchMode.AUTOMATIC }; medianFilter3dAlgo.Execute(); Console.WriteLine( "outputImage:" + medianFilter3dAlgo.outputImage.ToString() );
Function Examples
auto foam = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam.vip" ); auto result = medianFilter3d( foam, 3, MedianFilter3d::KernelMode::CUBE, MedianFilter3d::SearchMode::AUTOMATIC ); std::cout << "outputImage:" << result->toString();
foam = imagedev.read_vip_image(imagedev_data.get_image_path("foam.vip")) result = imagedev.median_filter_3d(foam, 3, imagedev.MedianFilter3d.CUBE, imagedev.MedianFilter3d.AUTOMATIC) print("output_image:", str(result))
ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" ); IOLink.ImageView result = Processing.MedianFilter3d( foam, 3, MedianFilter3d.KernelMode.CUBE, MedianFilter3d.SearchMode.AUTOMATIC ); Console.WriteLine( "outputImage:" + result.ToString() );