Image enhancement in the frequency domain is straightforward. We simply compute the Fourier transform of the image to be enhanced, multiply the result by a filter (rather than convolve in the spatial domain), and take the inverse transform to produce the enhanced image.

The idea of blurring an image by reducing its high frequency components, or sharpening an image by increasing the magnitude of its high frequency components is intuitively easy to understand. However, computationally, it is often more efficient to implement these operations as convolutions by small spatial filters in the spatial domain. Understanding frequency domain concepts is important, and leads to enhancement techniques that might not have been thought of by restricting attention to the spatial domain.

Images normally consist of light reflected from objects. The basic
nature of the image *F*(*x*,*y*) may be characterized by two components:
(1) the amount of source light incident on the scene being viewed, and
(2) the amount of light reflected by the objects in the scene. These
portions of light are called the *illumination* and *
reflectance* components, and are denoted *i*(*x*,*y*) and *r*(*x*,*y*)
respectively. The functions *i* and *r* combine
multiplicatively to give the
image function *F*:

*F*(*x*,*y*) = *i*(*x*,*y*)*r*(*x*,*y*),

Then

or

whereIf we now apply a filter with a transfer function that suppresses low frequency components and enhances high frequency components, then we can suppress the illumination component and enhance the reflectance component. Thus

where *S* is the Fourier transform of the result. In the
spatial domain

By letting

and we get
*s*(*x*,*y*) = *i*'(*x*,*y*) + *r*'(*x*,*y*).

Thus, the process of homomorphic filtering can be summarized by the following diagram: