.. _`Denoise image`:

.. _`com.sympathyfordata.imageanalysis.image_denoise`:

Denoise image
`````````````

.. image:: image_denoise.svg
   :width: 48


Perform image denoising




Documentation
:::::::::::::

Algorithms
==========

    **Bilateral**
        Edge-preserving denoising filter


        :window size: Window size for filtering

        :sigma color: Standard deviation for grey/color distance. Larger values resultsin averaging of pixels with larger grey/color differences. If 0.0 then compute and use the standard deviation in the image.

        :sigma spatial: Standard deviation for range distances. A larger value results in averaging pixels that are further apart.

        :bins: Number of discrete values for gaussian weights of color filtering

        :edge mode: How to handle values outside the image borders

        :cval: Value used when mode is constant

        :multichannel: Wheter to treat the channels as colors or a separate spatial dimension




    **Non-local means**
        A de-noising technique suited for images with specific textures. It finds other pixels in a neighbourhood that have a similartexture within the given patch size, and computes the average ofthese pixels.


        :sigma: Optional: standard deviation of a presumed white gaussian noise

        :patch size: Size of the texture area that must match

        :patch distance: Maximum distance to search for similar pixels

        :h: Cut-off distance (grey levels). Higher values accept more patches.

        :fast: Uses an alternative faster algorithm

        :multichannel: Process multiple channels at the same time




    **Total variation Bregman**
        Total variation denoising using split-Bregman optimization


        :weight: Controls amount of denoising performed. Smaller values give higher denoising

        :iter: Maximum number of iterations of algoritm

        :isotropic: If false use anisotropic filtering instead




    **Total variation Chambolle**
        Total variation denoising using split-Bregman optimization


        :weight: Controls amount of denoising performed. Greater values give higher denoising

        :iter: Maximum number of iterations of algoritm




    **Wavelet**
        Wavelet denoising


        :sigma: Estimated standard deviation of noise, if 0.0 then an automatic estimate  is calculated

        :wavelet levels: Number of wavelet decomposition levels to use, if 0 then use 3
            less levels than the maximum given the image size

        :wavelet: Type of wavelet to calculate

        :mode: Type of denoising to perform, soft is best for additive noise

        :convert to YCbCr: If true and if multichannel is true, then convert RGB to the YCbCr colorspace.

        :method: Thresholding method to use

        :multichannel: Applies denoising for each channel separately vs. together


Definition
::::::::::


Input ports
===========

    **source**  image
        source image to denoise


Output ports
============

    **result**  image
        result after denoising




Configuration
=============

    **Algorithm** (algorithm)
        (no description)
    **bins** (bins)
        (no description)
    **convert to YCbCr** (convert to YCbCr)
        (no description)
    **cval** (cval)
        (no description)
    **edge mode** (edge mode)
        (no description)
    **fast** (fast)
        (no description)
    **h** (h)
        (no description)
    **isotropic** (isotropic)
        (no description)
    **iter** (iter)
        (no description)
    **method** (method)
        (no description)
    **mode** (mode)
        (no description)
    **multichannel** (multichannel)
        (no description)
    **patch distance** (patch distance)
        (no description)
    **patch size** (patch size)
        (no description)
    **sigma** (sigma)
        (no description)
    **sigma color** (sigma color)
        (no description)
    **sigma spatial** (sigma spatial)
        (no description)
    **wavelet** (wavelet)
        (no description)
    **wavelet levels** (wavelet levels)
        (no description)
    **weight** (weight)
        (no description)
    **window size** (window size)
        (no description)









Implementation
==============

.. automodule:: node_noise
    :noindex:

.. class:: ImageDenoise
    :noindex: