The purpose of image restoration is to "compensate for" or "undo" defects
which degrade an image. Degradation comes in many forms such as motion
blur, noise, and camera misfocus. In cases like motion blur, it is
possible to come up with an very good estimate of the actual blurring
function and "undo" the blur to restore the original image. In cases
where the image is corrupted by noise, the best we may hope to do is to
compensate for the degradation it caused. In this project, we will
introduce and implement several of the methods used in the image
processing world to restore images.
The block diagram for our general degradation model is
where g is the corrputed image obtained by passing the original
image f through a low pass filter (blurring fuction) b and
adding noise to it. We present four different ways of restoring the
In this method we look at an image assuming a known blurring function.
We will see that restoration is good when noise is not present and not so
good when it is.
In this section we implement image restoration using wiener filtering,
which provides us with the optimal trade-off between de-noising and
inverse filtering. We will see that the result is in general better than
with straight inverse filtering.
We implement three wavelet bassed algorithms to restore the image.
In this method, we assume nothing about the image. We do not have any
information about the blurring function or on the additive noise. We will
see that restoring an image when we know nothing about it is very hard.
Nothing but the conclusions.