Load the *house.l.aiff* image from the `images` directory.

Apply the `gauss` function in the image calculator to the image.
Remember you have to choose a suitable width using the appropriate parameters
in `imcalc params` (Hint: use the `help` button to identify the
parameters that `gauss` uses). Use a `prof` graph to view the affect
on image structure (such as edges) and the `hist` and `noise`
functions to see how it affects image noise.
Repeat this for various scales of smoothing starting at a kernal width (**sigma**)
of 1 and
increasing to 5 (you must also remember to adjust the **range** parameter to accomodate
this larger filter). Don't make the kernel too big as the execution time can increase
dramatically.

Use the `gauss` function to enhance the edges of the image. Do this using
the difference of Gaussian (DoG) approximation to the Laplacian.
Can you produce a binary image showing the edge locations you have found?

Repeat the above in the frequency domain, remembering that convolution in one
domain is multiplication in the other. You will need to construct an image
of the Gaussian kernel of the same size as the original image (Hint: use
the `delta` in the imcalc `create tool`).

Can you see any appreciable difference between the results?
^{4.1}.

Finally attempt a Fourier deconvolution of the smoothed image (read carefully the section in the manual regarding division using "imcalc").

Attempt to answer the following questions;

- How do results compare: firstly for Fourier convolved images (at a range of scales)
and secondly for spatially convolved images?
- Can you explain why this might happen?
- What are the consequences for the application of deconvolution to tasks such as image
deblurring?