.. _Smoothing: 

Smoothing
=========





Apply a smoothing filter to an image

Description
-----------

This application applies a smoothing filter to an image. Three methods can be used: a mean filter, a gaussian filter based on [1], or an anisotropic diffusion using the Perona-Malik algorithm [2].



Parameters
----------

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**Input Image** :code:`-in image` *Mandatory* |br|
Input image to smooth.

**Output Image** :code:`-out image [dtype]` *Mandatory* |br|
Output smoothed image.

**Smoothing Type** :code:`-type [mean|gaussian|anidif]` *Default value: anidif* |br|
Smoothing kernel to apply

* **Mean** |br| 

* **Gaussian** |br| 

* **Anisotropic Diffusion** |br| 



Mean options
^^^^^^^^^^^^

**Radius** :code:`-type.mean.radius int` *Default value: 2* |br|
Kernel's radius (in pixels)

Gaussian options
^^^^^^^^^^^^^^^^

**Standard deviation** :code:`-type.gaussian.stdev float` *Default value: 2* |br|
Standard deviation of the gaussian kernel used to filter the image

**Maximum error** :code:`-type.gaussian.maxerror float` *Default value: 0.01* |br|
The algorithm will size the discrete kernel so that the error resulting from truncation of the kernel is no greater than maxerror.

**Maximum kernel width** :code:`-type.gaussian.maxwidth int` *Default value: 32* |br|
Set the kernel to be no wider than maxwidth pixels, even if type.gaussian.maxerror demands it.

Anisotropic Diffusion options
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

**Time Step** :code:`-type.anidif.timestep float` *Default value: 0.125* |br|
Time step that will be used to discretize the diffusion equation

**Nb Iterations** :code:`-type.anidif.nbiter int` *Default value: 10* |br|
Number of iterations needed to get the result

**Conductance** :code:`-type.anidif.conductance float` *Default value: 1* |br|
Controls the sensitivity of the conductance term in the diffusion equation. The lower it is the stronger the features will be preserved



------------

**Available RAM (MB)** :code:`-ram int` *Default value: 256* |br|
Available memory for processing (in MB).



Examples
--------

From the command-line:

.. code-block:: bash

    # Image smoothing using a mean filter.
    otbcli_Smoothing -in Romania_Extract.tif -out smoothedImage_mean.png uchar -type mean

    # Image smoothing using an anisotropic diffusion filter.
    otbcli_Smoothing -in Romania_Extract.tif -out smoothedImage_ani.png float -type anidif -type.anidif.timestep 0.1 -type.anidif.nbiter 5 -type.anidif.conductance 1.5


From Python:

.. code-block:: python

	# Image smoothing using a mean filter.
	import otbApplication

	app = otbApplication.Registry.CreateApplication("Smoothing")

	app.SetParameterString("in", "Romania_Extract.tif")
	app.SetParameterString("out", "smoothedImage_mean.png")
	app.SetParameterOutputImagePixelType("out", 1)
	app.SetParameterString("type","mean")

	app.ExecuteAndWriteOutput()

.. code-block:: python

	# Image smoothing using an anisotropic diffusion filter.
	import otbApplication

	app = otbApplication.Registry.CreateApplication("Smoothing")

	app.SetParameterString("in", "Romania_Extract.tif")
	app.SetParameterString("out", "smoothedImage_ani.png")
	app.SetParameterOutputImagePixelType("out", 6)
	app.SetParameterString("type","anidif")
	app.SetParameterFloat("type.anidif.timestep", 0.1)
	app.SetParameterInt("type.anidif.nbiter", 5)
	app.SetParameterFloat("type.anidif.conductance", 1.5)

	app.ExecuteAndWriteOutput()





See also
--------

| :ref:`FastNLMeans` 
| [1] Tony Lindeberg Discrete Scale-Space Theory and the Scale-Space Primal Sketch.  Dissertation. Royal Institute of Technology, Stockholm, Sweden. May 1991 
| [2] Pietro Perona and Jitendra Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 12, pp. 629-639, 1990. 
| itk::MeanImageFilter (mean mode)
| itk::DiscreteGaussianImageFilter (gaussian mode)
| itk::GradientAnisotropicDiffusionImageFilter (anidif mode).