A convolution is one of the convex folds of the surface of the brain, and you may feel like your brain is all crinkled up by convolutions, but they are quite useful.
A convolution is an image processing operation that operates by comparing neighboring pixels. Convolution filters are often encapsulated into one simple to use model called a convolution kernel that can perform a number of different functions that simulate real world effects.
The center value represents the center pixel, and the value it will be multiplied by. Each surrounding pixel is also multiplied by a value, and then all the pixels are added together. To 'normalize' , or to keep the final value within a usable range, it is then divided by the sum of all the values. The convolution kernel filter shown here automatically calculates the sum for us. As an option, an 'offset' value can be added. It is useful for certain times, such as when you want to have an image based around gray.
There are numerous effects that can be achieved with a convolution kernel. Included are blur, sharpen, edge detect, emboss, and more. Here are just a few examples:
| 1 | 1 | 1 | -1 | -1 | 0 | 1 | 1 | 1 | ||
| 1 | 1 | 1 | -1 | 0 | 1 | 1 | -8 | 1 | ||
| 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | ||
| Blur (box filter) | Emboss | Edge detect | ||||||||
