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# Gaussian Convolution

StreamableKNIME Image Types and Nodes version 1.8.1.202003280535 by University of Konstanz

A Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function. It is a widely used effect in graphics software, typically to reduce image noise and reduce detail. The visual effect of this blurring technique is a smooth blur resembling that of viewing the image through a translucent screen, distinctly different from the bokeh effect produced by an out-of-focus lens or the shadow of an object under usual illumination. Gaussian smoothing is also used as a pre-processing stage in computer vision algorithms in order to enhance image structures at different scales—see scale space representation and scale space implementation (from http://en.wikipedia.org/wiki/Gaussian_blur).

## Options

Sigmas
Standard deviation used to create the Gaussian in each dimension. A sigma can be specified for each dimension (simple seperate the values with commas). If only a single value is given, it will be used for each dimension. If multiple sigmas are given, the number of sigmas must match the number of selected dimensions.
Dimension Selection

This component allows the selection of dimensions of interest. If an algorithm cannot, as it only supports fewer dimensions than the number of dimensions of the image, or shouldnot, as one wants to run the algorithm only on subsets of the image, be applied on the complete image, the dimension selection can be used to define the plane/cube/hypercube on which the algorithm is applied. Example 1 with three dimensional Image (X,Y,Time): An algorithm cannot be applied to the complete image, as it only supports two dimensional images. If you select e.g. X,Y then the algorithm will be applied to all X,Y planes individually. Example 2 with three dimensional Image (X,Y,Time): The algorithm can be applied in two, three or even more dimensions. Select the dimensions to define your plane/cube/hypercube on which the algorithm will be applied.

#### Column Selection

Column Creation Mode

Mode how to handle the selected column. The processed column can be added to a new table, appended to the end of the table, or the old column can be replaced by the new result

Column Suffix
A suffix appended to the column name. If "Append" is not selected, it can be left empty.
Column Selection
Selection of the columns to be processed.
Out of Bounds Selection

The 'OutOfBounds Strategy' is used when an algorithm needs access to pixels which lie outside of an image (for example convolutions). The strategy determines how an image is extended, for examples see here

Images

Convolved Images

## Views

Image Viewer
Another, possibly interactive, view on table cells. Displays the selected cells with their associated viewer if it exists. Available views are:
- Image Viewer
-- This viewer renders the selected image-cell.
- Histogram Viewer
-- This viewer shows the histogram of the currently selected image.
- Missing Value Viewer
-- An empty viewer that is shown when the input cell has no value to display.
- BigDataViewer
-- A viewer shown when the user selects an interval of rows and columns in the viewer. This viewer combines all images and labelings in the selected interval to one image by rendering them next to each other. Alternatively, the images and labelings can be layed over each other.
- Combined View
-- A viewer shown when the user selects an interval of rows and columns in the viewer. This viewer combines all images and labelings in the selected interval to one image by rendering them next to each other. Alternatively, the images and labelings can be layed over each other.
- XML
-- XML tree
- Labeling View
-- View on a labeling/segmentation

## Installation

To use this node in KNIME, install KNIME Image Processing from the following update site:

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