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As opposed to [0..255]

Render with scanalyze????

E.g., gray scale, contrast enhancement (gamma), image thresholding, etc...

tx = 2x, ty = y

Then, how about ty = -y/3?

show image morphing applet: http://www.colonize.com/warp/

Inverse is also linear

Salt and pepper and impulse noise can be due to transmission errors (e.g., from deep space probe), dead CCD pixels, specks on lens

We’re going to focus on Gaussian noise first.

If you had a sensor that was a little noisy and measuring the same thing over and over, how would you reduce the noise?

Replace each pixel with the average of a kxk window around it

Move around over previous slide

Replace each pixel with the average of a kxk window around it

What happens if we use a larger filter window?

Demo with photoshop

ones, divide by 9

They are the same for filters that have both horizontal and vertical symmetry.

For big filters, can be faster to convert to fourier domain, then multiply, then convert back

Better at salt’n’pepper noise

Not convolution: try a region with 1’s and a 2, and then 1’s and a 3