## Brief Description

Mean filtering is a simple, intuitive and easy to implement method of *smoothing* images, *i.e.* reducing the amount of intensity variation between one pixel and the next.

## How It Works

The idea of mean filtering is simply to replace each pixel value in an image with the mean (`average’) value of its neighbors, including itself. This has the effect of eliminating pixel values which are unrepresentative of their surroundings. Mean filtering is usually thought of as a convolution filter. Like other convolutions it is based around a kernel, which represents the shape and size of the neighborhood to be sampled when calculating the mean. Often a 3×3 square kernel is used, as shown in Figure 1, although larger kernels (*e.g.* 5×5 squares) can be used for more severe smoothing. (Note that a small kernel can be applied more than once in order to produce a similar but not identical effect as a single pass with a large kernel.)

Figure 13×3 averaging kernel often used in mean filtering

Computing the straightforward convolution of an image with this kernel carries out the mean filtering process.

## Common Variants

Variations on the mean smoothing filter discussed here include *Threshold Averaging* wherein smoothing is applied subject to the condition that the center pixel value is changed only if the difference between its original value and the average value is greater than a preset threshold. This has the effect that noise is smoothed with a less dramatic loss in image detail.

Other convolution filters that do not calculate the mean of a neighborhood are also often used for smoothing. One of the most common of these is the Gaussian smoothing filter.

**Basic Working:**

The effect of a mean Filter on the Image:

The result after convolution is as:

**Sample Project:**

The GUI of the whole Project is as:

The project is a part of the series of the image processing articles written just for the prosperity and help for the students searching for Image Processing free stuff.

## References

**R. Boyle and R. Thomas** *Computer Vision: A First Course*, Blackwell Scientific Publications, 1988, pp 32 – 34.

**E. Davies** *Machine Vision: Theory, Algorithms and Practicalities*, Academic Press, 1990, Chap. 3.

**D. Vernon** *Machine Vision*, Prentice-Hall, 1991, Chap. 4.

It is often used to reduce noise in images. It is the most common of all the filtering techniques.