Filter the input image using a bilateral filter. Wikipedia: The intensity value at each pixel in an image is replaced by a weighted average of intensity values from nearby pixels. This weight can be based on a Gaussian distribution. Crucially, the weights depend not only on Euclidean distance of pixels, but also on the radiometric differences. For example, the range difference such as color intensity, depth distance, etc. This preserves sharp edges by systematically looping through each pixel and adjusting weights to the adjacent pixels accordingly.
The size of the span which will be used by the algorithm.
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.
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
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To use this node in KNIME, install the extension KNIME Image Processing from the below update site following our NodePit Product and Node Installation Guide:
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