Image Processing Toolbox User's Guide

Noise Removal

Digital images are prone to a variety of types of noise. Noise is the result of errors in the image acquisition process that result in pixel values that do not reflect the true intensities of the real scene. There are several ways that noise can be introduced into an image, depending on how the image is created. For example:

• If the image is scanned from a photograph made on film, the film grain is a source of noise. Noise can also be the result of damage to the film, or be introduced by the scanner itself.
• If the image is acquired directly in a digital format, the mechanism for gathering the data (such as a CCD detector) can introduce noise.
• Electronic transmission of image data can introduce noise.

The toolbox provides a number of different ways to remove or reduce noise in an image. Different methods are better for different kinds of noise. The methods available include

• Using Linear Filtering
• Using Median Filtering
• Using Adaptive Filtering

To simulate the effects of some of the problems listed above, the toolbox provides the imnoise function, which you can use to add various types of noise to an image. The examples in this section use this function.

Decorrelation Stretching

Using Linear Filtering

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