Due to many occurrences in the natural environment, it becomes necessary to
restore an image that was taking poorly or degraded due to noise in
transmission. Not only is it possible to properly view a noisy picture, but it
also possible through Digital Signal Processing Techniques to restore a picture
that is blurry, and or out-of-focus.
In this project we used MATLAB to handle all of our signal processing. All
of our test images were either grayscale or converted to grayscale, as a result
of the way in which matlab stores images. This is our basic model for a
G = F * H + N.
One of our methods for restoring images was the use of inverse filtering.
In the absence of noise the equation becomes G = F * H. Thus you can see that
F = G/H. Thus given G, we can reproduce F, if we can model the distortion
filter accurately enough. However this is the absolute absence of noise,
because other wise inverse filtering results in (F + N)/H. Inverse filtering
the noise, is generally considered to be bad.
The wiener filtering method has the advantage over inverse filtering in
that it can work on noisy distorted images. It has the form:
Wf = ----------------
H *|H^2| + K
These methods will reduce the amount of noise contained in a signal, but
will do nothing towards restoring a distorted image. It is a low-pass
filtering method based on the assumption that each pixel is more or less like
the ones around it. As a result, fine details may be loss, but a better feel
for the entire image may be gained.
Image processing good.