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The pixels in an input image represent different “spatial” positions,
therefore when convolution is done only using the actual input pixel
values, we name the process as being done in the “Spatial domain”. In
particular this is in contrast to the “frequency domain” that we will
discuss later in Frequency domain and Fourier operations. In the
spatial domain (and in realistic situations where the image and the
convolution kernel don’t extend to infinity), convolution is the process of
changing the value of one pixel to the *weighted* average of all the
pixels in its *neighborhood*.

The ‘neighborhood’ of each pixel (how many pixels in which direction) and
the ‘weight’ function (how much each neighboring pixel should contribute
depending on its position) are given through a second image which is known
as a “kernel”^{95}.

• Convolution process: | More basic explanations. | |

• Edges in the spatial domain: | Dealing with the edges of an image. |

GNU Astronomy Utilities 0.7 manual, August 2018.