Transforms
The usual mathematical representation of an image is a function
of two spatial variables:
. The value of the function at a particular location
represents the
intensity of the image at that point. This is called the spatial domain.
The term transform refers to an alternative mathematical
representation of an image. For example, the Fourier transform is
a representation of an image as a sum of complex exponentials of varying
magnitudes, frequencies, and phases. This is called the frequency
domain. Transforms are useful for a wide range of purposes,
including convolution, enhancement, feature detection, and compression.
This chapter defines several important transforms and shows
examples of their application to image processing.
 | Designing Linear Filters in the Frequency Domain | | Fourier Transform |  |
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