In the previous activity, we restored images that have additive noise.
Now we add another kind of degradation which is the motion blur.
The degradation/restoration process can be described by the following model.

In the spatial domain, the degradation process is a convolution with the image.


Suppose that this degradation is due to the uniform linear motion between the image and the sensor or camera. Then the camera would have captured the image motion in the duration of the exposure time T.



displaced in the x- and y-direction, respectively. (Note: xo(t) = at/T and
yo(t) = bt/T)
WEINER FILTERING
This method considers images and noise as random processes and tries to minimize the MSE between orignal f and restored f_hat images using the following expression known as the Weiner filter



For our original image we use

Next we degrade the original image by blurring (transfer function given by Eq5) and adding Gaussian noise (Activity 18). We can get the degraded image using Eq2.
The parameters a, b, and T will determine the velocity of the motion in the x and y direction: vx=a/T and vy=b/T. Of course, increasing the velocities would just make the image more blurry as shown in the following images.
Doing the restoration using the actual noise pdf ( eqn 6 ), we get the following results.
Of course, the more blurred an image is, the less perfect the restoration. However we note that even for a=b=0.1 blurring, when image is no longer recognizable, filtering was still able to salvage some of the lost data.

Now we compare the results when we just assume white noise (Eq 7). As expected. better results were obtained when we did not assume white noise. K=0 results to almost all noise images. The filtering improved when K=0.001 and increasing K further does not seem to result to better restorations.

In this activity, I was able to do reconstructions of very blurry images so I give myself a grade of 10.
The parameters a, b, and T will determine the velocity of the motion in the x and y direction: vx=a/T and vy=b/T. Of course, increasing the velocities would just make the image more blurry as shown in the following images.

Of course, the more blurred an image is, the less perfect the restoration. However we note that even for a=b=0.1 blurring, when image is no longer recognizable, filtering was still able to salvage some of the lost data.

Now we compare the results when we just assume white noise (Eq 7). As expected. better results were obtained when we did not assume white noise. K=0 results to almost all noise images. The filtering improved when K=0.001 and increasing K further does not seem to result to better restorations.

In this activity, I was able to do reconstructions of very blurry images so I give myself a grade of 10.