ImageDev

NagaoFilter3d

Performs an edge-preserving smoothing of a three-dimensional image by selecting a mean value from different neighborhood configurations.

Access to parameter description

For an introduction to image filters: see Images Filtering.

This algorithm uses an adaptive 5x5 neighborhood geometry to compute a mean neighbors value around a central pixel.
The mean values of different neighborhood configurations around the considered pixel are computed. The pixel is replaced by the mean value of the configuration presenting: This criterion is user-defined by the filter method parameter.

The predefined sub neighborhoods are composed of 27 pixels and are a 3D extension of the Nagmod configurations of the NagaoFilter2d algorithm.

Reference:
M.Nagao, T.Matsuyama. "Edge preserving smoothing". Computer Graphics and Image Processing, vol. 9, pp. 394-407, 1979.

See also

Function Syntax

This function returns outputImage. 
// Function prototype
std::shared_ptr< iolink::ImageView >
nagaoFilter3d( std::shared_ptr< iolink::ImageView > inputImage,
               NagaoFilter3d::FilterMethod filterMethod,
               std::shared_ptr< iolink::ImageView > outputImage = NULL );
This function returns outputImage. 
// Function prototype.
nagao_filter_3d( input_image, filter_method = NagaoFilter3d.FilterMethod.VARIANCE, output_image = None )
This function returns outputImage. 
// Function prototype.
public static IOLink.ImageView
NagaoFilter3d( IOLink.ImageView inputImage,
               NagaoFilter3d.FilterMethod filterMethod = ImageDev.NagaoFilter3d.FilterMethod.VARIANCE,
               IOLink.ImageView outputImage = null );

Class Syntax

Parameters

Class Name NagaoFilter3d

Parameter Name Description Type Supported Values Default Value
input
inputImage
The input image. Image Binary, Label, Grayscale or Multispectral nullptr
input
filterMethod
The neighborhood selection criterion.
VARIANCE This option selects the mean of the neighborhood configuration of lower variance.
RANGE This option selects the mean of the neighborhood configuration of lower intensity range.
Enumeration VARIANCE
output
outputImage
The output image. Its dimensions, type, and calibration are forced to the same values as the input. Image nullptr

Object Examples

auto foam = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam.vip" );

NagaoFilter3d nagaoFilter3dAlgo;
nagaoFilter3dAlgo.setInputImage( foam );
nagaoFilter3dAlgo.setFilterMethod( NagaoFilter3d::FilterMethod::VARIANCE );
nagaoFilter3dAlgo.execute();

std::cout << "outputImage:" << nagaoFilter3dAlgo.outputImage()->toString();

foam = imagedev.read_vip_image(imagedev_data.get_image_path("foam.vip"))

nagao_filter_3d_algo = imagedev.NagaoFilter3d()
nagao_filter_3d_algo.input_image = foam
nagao_filter_3d_algo.filter_method = imagedev.NagaoFilter3d.VARIANCE
nagao_filter_3d_algo.execute()

print( "output_image:", str( nagao_filter_3d_algo.output_image ) )

ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" );

NagaoFilter3d nagaoFilter3dAlgo = new NagaoFilter3d
{
    inputImage = foam,
    filterMethod = NagaoFilter3d.FilterMethod.VARIANCE
};
nagaoFilter3dAlgo.Execute();

Console.WriteLine( "outputImage:" + nagaoFilter3dAlgo.outputImage.ToString() );

Function Examples

auto foam = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam.vip" );

auto result = nagaoFilter3d( foam, NagaoFilter3d::FilterMethod::VARIANCE );

std::cout << "outputImage:" << result->toString();

foam = imagedev.read_vip_image(imagedev_data.get_image_path("foam.vip"))

result = imagedev.nagao_filter_3d( foam, imagedev.NagaoFilter3d.VARIANCE )

print( "output_image:", str( result ) )

ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" );

IOLink.ImageView result = Processing.NagaoFilter3d( foam, NagaoFilter3d.FilterMethod.VARIANCE );

Console.WriteLine( "outputImage:" + result.ToString() );