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.|
Also known as filter, here we will use ‘kernel’.