Dirkjan at 20:16, 27 August 2020

2020-08-27T20:16:42Z

Dirkjan: Created page with "For simplicity, assume that the image I being considered is formed by projection from scene S (which might be a two- or three-dimensional scene, etc.). The frequency do..."

2020-08-25T09:03:00Z

Created page with "<html>For simplicity, assume that the image I being considered is formed by projection from scene S (which might be a two- or three-dimensional scene, etc.). The frequency do..."

New page

<html>For
simplicity, assume that the image I being considered is formed by
projection from scene S (which might be a two- or three-dimensional
scene, etc.).

The frequency domain is a space in which each image
value at image position F represents the amount that the intensity
values in image I vary over a specific distance related to F. In the frequency domain, changes in image position
correspond to changes in the spatial frequency, (or the rate at which
image intensity values) are changing in the [[spatial domain|spatial domain]] image
I. For example, suppose that there is the value
20 at the point that represents the frequency 0.1 (or 1 period every 10
pixels). This means that in the corresponding spatial domain image I
the intensity values vary from dark to light and back to dark over a
distance of 10 pixels, and that the contrast between the lightest and
darkest is 40 gray levels (2 times 20).

The [[spatial frequency|spatial frequency]]
domain is interesting because: 1) it may make
explicit periodic relationships in the spatial domain, and 2) some image processing operators are more efficient or indeed
only practical when applied in the frequency domain. In most cases, the Fourier Transform is used to convert images
from the spatial domain into the frequency domain and vice-versa.

A
related term used in this context is [[spatial frequency|spatial frequency]], which
refers to the (inverse of the) periodicity with which the image
intensity values change. Image features with high spatial frequency
(such as edges) are those that change greatly in intensity over short
image distances. 
If an image has large values at high frequency components then
the data is changing rapidly on
a short distance scale. e.g. a page
of text

If the image has large low frequency components then
the large scale features of the
picture are more important. e.g. a
single fairly simple object which occupies most of the
image.
</html>
[[Category:microscopy]]

[[Category:image]]

[[Category:processing]]

@@ Line 9: / Line 9: @@
 /><br />In the frequency domain, changes in image position
 correspond to changes in the spatial frequency, (or the rate at which
-image intensity values) are changing in the [[spatial domain|spatial domain]] image
+image intensity values) are changing in the [[spatial domain]] image
 I.<br /><br />For example, suppose that there is the value
 at the point that represents the frequency 0.1 (or 1 period every 10
@@ Line 17: / Line 17: @@
 darkest is 40 gray levels (2 times 20).
-The [[spatial frequency|spatial frequency]]
+The [[spatial frequency]]
 domain is interesting because:<br /><br />1) it may make
 explicit periodic relationships in the spatial domain, and<br
@@ Line 26: / Line 26: @@
+A
-related term used in this context is [[spatial frequency|spatial frequency]], which
+related term used in this context is [[spatial frequency]], which
 refers to the (inverse of the) periodicity with which the image
 intensity values change. Image features with high spatial frequency

Frequency domain - Revision history

Dirkjan at 20:16, 27 August 2020

Dirkjan: Created page with "For simplicity, assume that the image I being considered is formed by projection from scene S (which might be a two- or three-dimensional scene, etc.). The frequency do..."