shock_filtershock_filterShockFilterShockFilter (Operator)


shock_filtershock_filterShockFilterShockFilter — Apply a shock filter to an image.


shock_filter(Image : SharpenedImage : Theta, Iterations, Mode, Sigma : )

Herror shock_filter(const Hobject Image, Hobject* SharpenedImage, double Theta, const Hlong Iterations, const char* Mode, double Sigma)

Herror T_shock_filter(const Hobject Image, Hobject* SharpenedImage, const Htuple Theta, const Htuple Iterations, const Htuple Mode, const Htuple Sigma)

void ShockFilter(const HObject& Image, HObject* SharpenedImage, const HTuple& Theta, const HTuple& Iterations, const HTuple& Mode, const HTuple& Sigma)

HImage HImage::ShockFilter(double Theta, Hlong Iterations, const HString& Mode, double Sigma) const

HImage HImage::ShockFilter(double Theta, Hlong Iterations, const char* Mode, double Sigma) const

HImage HImage::ShockFilter(double Theta, Hlong Iterations, const wchar_t* Mode, double Sigma) const   (Windows only)

static void HOperatorSet.ShockFilter(HObject image, out HObject sharpenedImage, HTuple theta, HTuple iterations, HTuple mode, HTuple sigma)

HImage HImage.ShockFilter(double theta, int iterations, string mode, double sigma)


The operator shock_filtershock_filterShockFilterShockFilterShockFilter applies a shock filter to the input image ImageImageImageImageimage to sharpen the edges contained in it. The principle of the shock filter is based on the transport of the gray values of the image towards an edge from both sides through dilation and erosion and satisfies the differential equation on the function u defined by the gray values in ImageImageImageImageimage at a time . The discretized equation is solved in IterationsIterationsIterationsIterationsiterations time steps of length ThetaThetaThetaThetatheta, so that the output image SharpenedImageSharpenedImageSharpenedImageSharpenedImagesharpenedImage contains the gray value function at the time .

The decision between dilation and erosion is made using the sign function s with values {-1,0,+1} on a conventional edge detector. The detector of Canny is available with ModeModeModeModemode='canny'"canny""canny""canny""canny" and the detector of Marr/Hildreth (the Laplace operator) can be selected by ModeModeModeModemode='laplace'"laplace""laplace""laplace""laplace".

To make the edge detection more robust, in particular on noisy images, it can be performed on a smoothed image matrix. This is done by giving the standard deviation of a Gaussian kernel for convolution with the image matrix in the parameter SigmaSigmaSigmaSigmasigma.


Note that filter operators may return unexpected results if an image with a reduced domain is used as input. Please refer to the chapter Filters.

Execution Information


ImageImageImageImageimage (input_object)  (multichannel-)image(-array) objectHImageHImageHobject (byte / uint2 / real)

Input image.

SharpenedImageSharpenedImageSharpenedImageSharpenedImagesharpenedImage (output_object)  image(-array) objectHImageHImageHobject * (byte / uint2 / real)

Output image.

ThetaThetaThetaThetatheta (input_control)  real HTupleHTupleHtuple (real) (double) (double) (double)

Time step.

Default value: 0.5

Suggested values: 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7

Restriction: 0 < Theta <= 0.7

IterationsIterationsIterationsIterationsiterations (input_control)  integer HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Number of iterations.

Default value: 10

Suggested values: 1, 3, 10, 100

Restriction: Iterations >= 1

ModeModeModeModemode (input_control)  string HTupleHTupleHtuple (string) (string) (HString) (char*)

Type of edge detector.

Default value: 'canny' "canny" "canny" "canny" "canny"

List of values: 'canny'"canny""canny""canny""canny", 'laplace'"laplace""laplace""laplace""laplace"

SigmaSigmaSigmaSigmasigma (input_control)  real HTupleHTupleHtuple (real) (double) (double) (double)

Smoothing of edge detector.

Default value: 1.0

Suggested values: 0.0, 0.5, 1.0, 2.0, 5.0

Restriction: Theta >= 0


F. Guichard, J. Morel; “A Note on Two Classical Shock Filters and Their Asymptotics”; Michael Kerckhove (Ed.): Scale-Space and Morphology in Computer Vision, LNCS 2106, pp. 75-84; Springer, New York; 2001.
G. Aubert, P. Kornprobst; “Mathematical Problems in Image Processing”; Applied Mathematical Sciences 147; Springer, New York; 2002.