Activity 18 - Noise Models and Basic Image Restoration

In this activity, we investigate how different noise reduction filters restore images with different kinds of added noise:

Noise models: Gaussian, Rayleigh, Gamma, Exponential, Uniform, and Impulse (or salt-and-pepper) Noise
Noise filters: Arithmetic Mean, Geometric Mean, Harmonic Mean, Contra-harmonic Mean

We used each of the above noise models in conjunction with each of the noise filters. Below are the results:


The arithmetic mean filter gave the cleanest restoration for all the noise models. The harmonic mean filter comes close, except for the salt and pepper noise. The harmonic mean is also better than arithmetic mean for the Gaussian noise. The geometric mean filter was the least effective filter. Except for the gamma noise, the geom mean filter converted the gray pixels to black.

I give myself 10 points for this activity. I was able to perform noise addition for different noise models, and restore the images using different noise filters.

Thanks to Kaye and Miguel for help in some of the noise models.