Understanding convolution

Convolution is an operation that operates on two images, one being an input image and the other one being a mask (also called the kernel) as a filter on the input image, producing an output image. 

Convolution filtering is used to modify the spatial frequency characteristics of an image. It works by determining the value of a central pixel by adding the weighted values of all of its neighbors together to compute the new value of the pixel in the output image. The pixel values in the output image are computed by traversing the kernel window through the input image, as shown in the next screenshot (for convolution with the valid mode; we'll see convolution modes later in this chapter):

As you can see, the kernel window, marked by an arrow in the input image, traverses through the image and obtains values that are mapped on the output image after convolving.

Why convolve an image?

Convolution applies a general-purpose filter effect on the input image. This is done in order...