ImageDev

RecursiveLaplacian3d

Computes the Laplacian of a three-dimensional image with a recursive algorithm.

Access to parameter description

For an introduction: This algorithm is a recursive implementation for the determination of the Laplacian operator.

To minimize the effect of noise, the RecursiveLaplacian3d module smooths the image while computing the Laplacian by applying a second order derivative of the Deriche smoothing filter which has the three-dimensional form: $$ f(x,y,z)=b^3(\alpha|x|+1)e^{-\alpha|x|}\cdot(\alpha|y|+1)e^{-\alpha|y|}\cdot(\alpha|z|+1) e^{-\alpha|z|}~~where~~b=\frac{\alpha}{4} $$
The smoothing scale parameter determines the smoothing intensity. If the value is large, the noise will be reduced but edges will be less sharp and only the most important edges will appear in the output image.
It is important to select the right coefficient to lower the noise just enough without defocusing the edges.

Reference
R.Deriche. "Using Canny's criteria to derive a recursively implemented optimal edge detector". International Journal of Computer Vision, vol.1, no 2, pp. 167-187, Jun. 1987.

See also

Function Syntax

This function returns the outputImage output parameter. 
// Function prototype.
std::shared_ptr< iolink::ImageView >
recursiveLaplacian3d( std::shared_ptr< iolink::ImageView > inputImage,
                      int32_t spreadValue,
                      std::shared_ptr< iolink::ImageView > outputImage = NULL );
This function returns the outputImage output parameter. 
// Function prototype.
recursive_laplacian_3d( input_image, spread_value = 60, output_image = None )
This function returns the outputImage output parameter. 
// Function prototype.
public static IOLink.ImageView
RecursiveLaplacian3d( IOLink.ImageView inputImage,
                      Int32 spreadValue = 60,
                      IOLink.ImageView outputImage = null );

Class Syntax

Parameters

Class Name RecursiveLaplacian3d

Parameter Name Description Type Supported Values Default Value
input
inputImage
The input image. Image Binary, Label, Grayscale or Multispectral nullptr
input
spreadValue
The smoothing factor defining the gradient sharpness. Its value must be between 0 and 100.
It is inversely proportional to Deriche alpha smoothing factor, in pixels. Low values provide sharp gradient. $SmoothingFactor=\frac{(5.3-\alpha)}{5}\times 100$ 		Int32 [0, 100] 60
output
outputImage
The output image. Image nullptr

Object Examples

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

RecursiveLaplacian3d recursiveLaplacian3dAlgo;
recursiveLaplacian3dAlgo.setInputImage( foam );
recursiveLaplacian3dAlgo.setSpreadValue( 60 );
recursiveLaplacian3dAlgo.execute();

std::cout << "outputImage:" << recursiveLaplacian3dAlgo.outputImage()->toString();

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

recursive_laplacian_3d_algo = imagedev.RecursiveLaplacian3d()
recursive_laplacian_3d_algo.input_image = foam
recursive_laplacian_3d_algo.spread_value = 60
recursive_laplacian_3d_algo.execute()

print( "output_image:", str( recursive_laplacian_3d_algo.output_image ) )

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

RecursiveLaplacian3d recursiveLaplacian3dAlgo = new RecursiveLaplacian3d
{
    inputImage = foam,
    spreadValue = 60
};
recursiveLaplacian3dAlgo.Execute();

Console.WriteLine( "outputImage:" + recursiveLaplacian3dAlgo.outputImage.ToString() );

Function Examples

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

auto result = recursiveLaplacian3d( foam, 60 );

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

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

result = imagedev.recursive_laplacian_3d( foam, 60 )

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

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

IOLink.ImageView result = Processing.RecursiveLaplacian3d( foam, 60 );

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