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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”^{70}.

• Convolution process: | More basic explanations. | |

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

GNU Astronomy Utilities 0.5 manual, December 2017.