**Common Names:** Mean filtering, Smoothing, Averaging, Box filtering

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. It is often used
to reduce noise in images.

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.

Mean filtering is most commonly used as a simple method for reducing noise in an image.

We illustrate the filter using

The image

shows the original corrupted by Gaussian noise with a mean of zero and a standard deviation () of 8.

The image

shows the effect of applying a 3×3 mean filter. Note that the noise is less apparent, but the image has been `softened'. If we increase the size of the mean filter to 5×5, we obtain an image with less noise and less high frequency detail, as shown in

The same image more severely corrupted by Gaussian noise (with a mean of zero and a of 13) is shown in

The image

is the result of mean filtering with a 3×3 kernel.

An even more challenging task is provided by

. It shows an image containing `salt and pepper' shot noise.

The image

shows the effect of smoothing the noisy image with a 3×3 mean filter. Since the shot noise pixel values are often very different from the surrounding values, they tend to significantly distort the pixel average calculated by the mean filter.

Using a 5×5 filter instead gives

This result is not a significant improvement in noise reduction and, furthermore, the image is now very blurred.

These examples illustrate the two main problems with mean filtering, which are:

- A single pixel with a very unrepresentative value can significantly affect the mean value of all the pixels in its neighborhood.
- When the filter neighborhood straddles an edge, the filter will interpolate new values for pixels on the edge and so will blur that edge. This may be a problem if sharp edges are required in the output.

Both of these problems are tackled by the median filter, which is often a better filter for reducing noise than the mean filter, but it takes longer to compute.

In general the mean filter acts as a lowpass frequency filter and, therefore, reduces the spatial intensity derivatives present in the image. We have already seen this effect as a `softening' of the facial features in the above example. Now consider the image

which depicts a scene containing a wider range of different spatial frequencies. After smoothing once with a 3×3 mean filter we obtain

Notice that the low spatial frequency information in the background has not been affected significantly by filtering, but the (once crisp) edges of the foreground subject have been appreciably smoothed. After filtering with a 7×7 filter, we obtain an even more dramatic illustration of this phenomenon in

Compare this result to that obtained by passing a 3×3 filter over the original image three times in

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.

You can interactively experiment with this operator by clicking here.

- The mean filter is computed using a convolution. Can
you think of any ways in which the special properties of the mean
filter kernel can be used to speed up the convolution? What is the
*computational complexity*of this faster convolution? - Use an edge detector on the image
and note the strength of the output. Then apply a 3×3 mean filter to the original image and run the edge detector again. Comment on the difference. What happens if a 5×5 or a 7×7 filter is used?

- Applying a 3×3 mean filter twice does not produce quite the same
result as applying a 5×5 mean filter once. However, a 5×5
convolution kernel
*can*be constructed which is equivalent. What does this kernel look like? - Create a 7×7 convolution kernel which has an equivalent effect to three passes with a 3×3 mean filter.
- How do you think the mean filter would cope with Gaussian noise which was not symmetric about zero? Try some examples.

**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.

Specific information about this operator may be found here.

More general advice about the local HIPR installation is available in the
*Local Information* introductory section.