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Images taken with both digital cameras and conventional film cameras will pick up noise from a variety of sources. Many further uses of these images require that the noise will be been (partially) removed - for aesthetic purposes as in artistic work or marketing, or for practical purposes such as computer vision.
Types of noise
In salt-and-pepper noise (also known as random or independent noise), pixels in the image are vastly different in color from their surrounding pixels. The defining characteristic is that the color of a noisy pixel bears no relation to the color of surrounding pixels. Generally this type of noise will only affect a small number of image pixels. When viewed, the image contains dark and white dots, hence the term salt and pepper noise. Typical sources include flecks of dust on the lens or inside the camera, or with digital cameras, fault CCD elements.
In Gaussian noise (dependent noise), an amount of noise is added to every part of the picture. Each pixel in the image will be changed from its original value by a (usually) small amount. Taking a plot of the amount of distortion of a pixel against the frequency with which it occurs produces a Gaussian distribution of noise.
Removing image noise
Gaussian masks
One method to remove noise is by convolving the original image with a mask. The Gaussian mask gives an image a blurred appearance, and has the effect of smearing out the value of a single pixel over an area of the image. This brings the value of each pixel into closer harmony with the value of it's neighbours. Gaussian masks work relatively well, but the blurring of edges can cause problems, particularly if the output is being fed into edge detection algorithms for computer vision applications.
Median filters
A median filter is very good at preserving image detail. To run a median filter:
consider each pixel in the image
sort the neighbouring pixels into order based upon their intensities
replace the original value of the pixel with the median value from the list
This type of filter is very good at removing salt and pepper noise from an image, and also causes very little blurring of edges, and hence is often used in computer vision applications.
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