Basics of spatial filtering

1. Basics of spatial filtering
Mr.S.Tamilselvan

2. Introduction
Spatial filtering is a process by which we can alter properties of an
optical image by selectively removing certain spatial frequencies that
make up an object, for example, filtering video data received from
satellite and space probes, or removal of raster from a television picture
or scanned image.

3. Why Use Spatial Filters?
Spatial filtering is commonly used to "clean up" the output of lasers,
removing aberrations in the beam due to imperfect, dirty, or damaged
optics, or due to variations in the laser gain medium itself.

4. Components of a Spatial Filter
● Input aperture.
● Lens system.
● Pinhole aperture.
● Output lens.

5. Function of the Input Aperture
the aperture does reduce the contrast of spatial frequencies that alias
and the close match between the spatial frequencies that alias and
filtering by the aperture suggests that detection and resolution of
gratings in peripheral retina is limited by aperture filtering

6. Lens System
In spatial filtering, a lens is used to focus the beam. Because of
diffraction, a beam that is not a perfect plane wave will not focus to a
single spot, but rather will produce a pattern of light and dark regions in
the focal plane.

7. Pinhole Aperture
Pinhole arrays and spatial filters are used for spatial filtering and act as
virtual point light sources in many optical systems. In this case, a
pinhole (pinhole aperture) limits the NA (divergence) and blocks larger
angles.

8. Output Lens
In spatial filtering techniques, the Fourier transform of an input function
that is obtained by a lens is manipulated by a filter. A second lens
performs the Fourier transform operation on the modified spectrum and
provides the output.

9. Practical Applications
● Laser systems.
● Microscopy.
● Astronomical observations.

10. Advantages and Limitations
● The filter operates directly on the pixels of the image, so the result
is easy to understand and interpret.
● The filter can be applied to small regions of the image, which
allows for more localized and fine-grained adjustments.
● Spatial filtering can be easily performed in real-time, making it
suitable for video and live-streaming applications.